Detecting neoplasm

ABSTRACT

This document relates to methods and materials for detecting premalignant and malignant neoplasms. For example, methods and materials for determining whether or not a stool sample from a mammal contains nucleic acid markers or polypeptide markers of a neoplasm are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 14/827,013, filed Aug. 14, 2015, which is a continuation of U.S. patent application Ser. No. 14/168,552, filed Jan. 30, 2014, now U.S. Pat. No. 9,121,070, which is a continuation of U.S. patent application Ser. No. 12/866,558, now U.S. Pat. No. 8,673,555, which is a Section 371 U.S. national stage entry of International Patent Application No PCT/US2009/033793, filed Feb. 11, 2009, which claims priority to U.S. Provisional Patent Application No. 61/029,221, filed Feb. 15, 2008, the contents of which are hereby incorporated by reference in their entireties.

BACKGROUND

1. Technical Field

This document relates to methods and materials involved in detecting premalignant and malignant neoplasms (e.g., colorectal and pancreatic cancer).

2. Background Information

About half of all cancer deaths in the United States result from aero-digestive cancer. For example, of the estimated annual cancer deaths, about 25 percent result from lung cancer, about 10 percent result from colorectal cancer; about 6 percent result from pancreas cancer, about 3 percent result from stomach cancer; and about 3 percent result from esophagus cancer. In addition, over 7 percent of the annual cancer deaths result from other aero-digestive cancers such as naso-oro-pharyngeal, bile duct, gall bladder, and small bowel cancers.

SUMMARY

This document relates to methods and materials for detecting premalignant and malignant neoplasms (e.g., colorectal and pancreatic cancer). For example, this document provides methods and materials that can be used to determine whether a sample (e.g., a stool sample) from a mammal contains a marker for a premalignant and malignant neoplasm such as a marker from a colonic or supracolonic aero-digestive neoplasm located in the mammal. The detection of such a marker in a sample from a mammal can allow a clinician to diagnose cancer at an early stage. In addition, the analysis of a sample such as a stool sample can be much less invasive than other types of diagnostic techniques such as endoscopy.

This document is based, in part, on the discovery of particular nucleic acid markers, polypeptide markers, and combinations of markers present in a biological sample (e.g., a stool sample) that can be used to detect a neoplasm located, for example, in a mammal's small intestine, gall bladder, bile duct, pancreas, liver, stomach, esophagus, lung, or naso-oro-pharyngeal airways. For example, as described herein, stool can be analyzed to identify mammals having cancer. Once a particular mammal is determined to have stool containing a neoplasm-specific marker or collection of markers, additional cancer screening techniques can be used to identify the location and nature of the neoplasm. For example, a stool sample can be analyzed to determine that the patient has a neoplasm, while magnetic resonance imaging (MRI), endoscopic analysis, and tissue biopsy techniques can be used to identify the location and nature of the neoplasm. In some cases, a combination of markers can be used to identify the location and nature of the neoplasm without additional cancer screening techniques such as MRI, endoscopic analysis, and tissue biopsy techniques.

In general, one aspect of this document features a method of detecting pancreatic cancer in a mammal. The method comprises, or consists essentially of determining the ratio of an elastase 3A polypeptide to a pancreatic alpha-amylase polypeptide present within a stool sample. The presence of a ratio greater than about 0.5 indicates that the mammal has pancreatic cancer. The presence of a ratio less than about 0.5 indicates that the mammal does not have pancreatic cancer.

In another aspect, this document features a method of detecting pancreatic cancer in a mammal. The method comprises or consists essentially of determining the level of an elastase 3A polypeptide in a stool sample from the mammal. The presence of an increased level of an elastase 3A polypeptide, when compared to a normal control level, is indicative of pancreatic cancer in the mammal.

In another aspect, this document features a method of detecting pancreatic cancer in a mammal. The method comprises, or consists essentially of, determining the level of a carboxypeptidase B polypeptide in a stool sample from the mammal. An increase in the level of a carboxypeptidase B polypeptide, when compared to a normal control level, is indicative of pancreatic cancer in the mammal.

In another aspect, this document features a method of detecting pancreatic cancer in a mammal. The method comprises, or consists essentially of, determining whether or not a stool sample from the mammal comprises a ratio of a carboxypeptidase B polypeptide to a carboxypeptidase A2 polypeptide that is greater than about 0.5. The presence of the ratio greater than about 0.5 indicates that the mammal has pancreatic cancer.

In another aspect, this document features a method of detecting cancer or pre-cancer in a mammal. The method comprises, or consists essentially of, determining whether or not a stool sample from the mammal has an increase in the number of DNA fragments less than 200 base pairs in length, as compared to a normal control. The presence of the increase in the number of DNA fragments less than 200 base pairs in length indicates that the mammal has cancer or pre-cancer. The DNA fragments can be less than 70 base pairs in length.

In another aspect, this document features a method of detecting colorectal cancer or pre-cancer in a mammal. The method comprises, or consists essentially of, determining whether or not a stool sample from the mammal has an elevated K-ras (Kirsten rat sarcoma-2 viral (v-Ki-ras2) oncogene homolog (GenBank accession no. NM_033360; gi|34485724|)) mutation score, an elevated BMP3 (bone morphogenetic protein 3 (GenBank accession no. M22491; gi|179505)) methylation status, and an elevated level of human DNA as compared to a normal control. The presence of the elevated K-ras mutation score, elevated BMP3 methylation status, and elevated level of human DNA level indicates that the mammal has colorectal cancer or pre-cancer. The K-ras mutation score can be measured by digital melt curve analysis. The K-ras mutation score can be measured by quantitative allele specific PCR.

In another aspect, this document features a method of detecting aero-digestive cancer or pre-cancer in a manual. The method comprises, or consists essentially of, determining whether or not a stool sample from the mammal has an elevated K-ras mutation score, an elevated BMP3 methylation status, and an elevated level of human DNA as compared to a normal control. The presence of the elevated K-ras mutation score, elevated BMP3 methylation status, and elevated level of human DNA level indicates that the mammal has aero-digestive cancer or pre-cancer. The K-ras mutation score can be measured by digital melt curve analysis. The K-ras mutation score can be measured by quantitative allele-specific PCR. The method can further comprise determining whether or not a stool sample from the mammal has an elevated APC mutation score. The APC mutation score can be measured by digital melt curve analysis.

In another aspect, this document features a method of detecting aero-digestive cancer or pre-cancer in a mammal. The method comprises, or consists essentially of determining whether or not the mammal has at least one mutation in six nucleic acids selected from the group consisting of p16, p53, k-ras. APC (adenomatosis polyposis coli tumor suppressor (GenBank accession no. NM_000038; gi|189011564)), SMAD4 (SMAD family member 4 (GenBank accession no. NM_005359; gi|195963400)). EGFR (epidermal growth factor receptor (GenBank accession no. NM_005228; gi|41327737)). CTNNB1 (catenin (cadherin-associated protein), beta 1 (88 kD) (GenBank accession no. X87838; gi|11548531)), and BRAF (B-Raf proto-oncogene serinethreonine-protein kinase (p94) (GenBank accession no. NM_004333; gi|1876086321)) nucleic acids. The presence of at least one mutation in each of the six nucleic acids indicates that the mammal has aero-digestive cancer or pre-cancer. The method can further comprise determining whether or not a stool sample from the mammal has an elevated level of a carboxvpeptidase B polypeptide as compared to a normal control. The presence of the elevated level of a caboxypeptidase B polypeptide indicates that the mammal has aero-digestive cancer or pre-cancer in the mammal. The method can further comprise determining whether or not a stool sample from the mammal has an elevated amount of DNA fragments less than 70 base pairs in length as compared to a normal control. The presence of the elevated amount of DNA fragments less than 70 base pairs in length indicates that the mammal has aero-digestive cancer or pre-cancer. The method can further comprise determining whether or not a stool sample from the mammal has an elevated amount of DNA fragments greater than 100 base pairs in length as compared to normal controls. The presence of the elevated amount of DNA fragments greater than 100) base pairs in length indicates that the mammal has aero-digestive cancer or pre-cancer. The method can further comprise determining whether or not a stool sample from the mammal has an elevated BMP3 methylation status. The elevated BMP3 methylation status level indicates that the mammal has aero-digestive cancer or pre-cancer. The determining step can comprise using digital melt curve analysis.

In another aspect, this document features a method of detecting aero-digestive cancer or pre-cancer in a mammal. The method comprises, or consists essentially of, measuring mutations in a matrix marker panel in a stool sample. The marker panel can comprise measuring DNA mutations in p16, p53, k-ras. APC, SMAD4. EGFR. CTNNB1, and BRAF nucleic acids. The presence of a mutation in each of the nucleic acids is indicative of the presence of aero-digestive cancer or pre-cancer in a mammal.

In another aspect, this document features a method of detecting aero-digestive cancer in a mammal. The method comprises, or consists essentially of, determining whether or not the methylation status of an ALX4 (aristaless-like homeobox 4 (GenBank accession no. AF294629; gi|10863748|)) nucleic acid in a stool sample from the mammal is elevated, as compared to a normal control. The presence of an elevated ALX4 methylation status indicates the presence of aero-digestive cancer in the mammal.

In another aspect, this document features a method of diagnosing pancreatic cancer in a mammal. The method comprises, or consists essentially of, obtaining a stool sample from the mammal, determining the ratio of an elastase 3A polypeptide to a pancreatic alpha-amylase polypeptide present within a stool sample, and communicating a diagnosis of pancreatic cancer if the ratio is greater than about 0.5, thereby diagnosing the mammal with pancreatic cancer.

In another aspect, this document features a method of diagnosing a mammal with pancreatic cancer. The method comprises, or consists essentially of, obtaining a stool sample from the mammal, measuring mutations in a matrix marker panel of nucleic acids present in the sample, determining the ratio of a carboxypeptidase B polypeptide to a carboxypeptidase A2 polypeptide present within the sample, and communicating a diagnosis of pancreatic cancer or pre-cancer if a mutation is detected in each of the marker panel nucleic acids and the ratio is greater than 0.5, thereby diagnosing the mammal. The matrix marker panel comprises or consists essentially of p16, p53, k-ras. APC, SMAD4. EGFR. CTNNB1, and BRAF nucleic acids.

In another aspect, this document features method of diagnosing a mammal with colorectal cancer. The method comprises, or consists essentially of, obtaining a stool sample from the mammal, detecting mutations in a matrix marker panel comprising of p16, p53, k-ras, APC. SMAD4. EGFR. CTNNB1, and BRAF nucleic acids in DNA present within the sample, measuring the level of a serotransferrin polypeptide present within the sample, and communicating a diagnosis of colorectal cancer or pre-cancer if a mutation is detected in each of the nucleic acids and the level of a serotransferrin polypeptide is elevated as compared to a reference level, thereby diagnosing the mammal.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1: Adjusted Cut-off Levels with Quantitative Stool Markers to Achieve 95% Specificity across Age and Gender using the Q-LEAD Model. Solid line for women, dotted line for men.

FIG. 2: Sensitive and specific detection of pancreatic cancer by fecal ratio of carboxypeptidase B:carboxypeptidase A2. Note that ratio in stools from patients with colorectal cancer is no different from ratios with healthy controls.

FIG. 3: Elastase levels quantified in stools from patients with pancreatic cancer, patients with colorectal cancer, and healthy controls.

FIG. 4: Ratio of elastase 3A:pancreatic alpha amylase differentiates patients with pancreatic cancer from patients with colorectal cancer and from healthy controls.

FIG. 5: Digital Melt Curve to detect mutations by targeted gene scanning (temperature (x-axis) v. temperature-normalized fluorescence (y-axis)). Eight pairs of primers, which amplify 100-350 bp gene fragments, were used to scan K-ras and APC genes and detect mutations (substitution and deletion mutations, respectively) at 1% mutant/wild type ratio.

FIG. 6: Quantitive detection of low abundance mutations by Digital Melt Curve Assay. Varying Mutant: Wild-type ratios of K-ras and APC gene mixtures were prepared and assayed blindly by Digital Melt Curve.

FIG. 7: High analytical sensitivity by Digital Melt Curve (temperature (x-axis) v. temperature-normalized fluorescence (y-axis)). To test the detection limit of digital melt curve (DMC) assay, mutant copies were spiked in wild-type copies at 0.1, 0.5, 1, 5, and 10% dilutions. DMC could detect up to 0.1% mutant/wild-type level when 1000 copies were dispersed to one 96-well plate. The numbers of positive wells increased proportionally when spiked mutant copies were increased. A pair of primers that amplify 248 bp K-ras gene fragment were used as an example here. Primers that amplify 119 bp K-ras gene, 162 bp APC gene, and 346 bp APC genes were also used to test the detection limit and quantitative property of DMC.

FIG. 8: Superior screen detection of colorectal precancerous polyps by Digital Melt Curve (DMC). Histogram compares sensitivity by DMC with that by common fecal occult blood tests (Hemoccult and HemoccultSENSA) and by the commercial stool DNA test (PreGenPlus, Exact Sciences). Detection by DMC was significantly better than by any other test (p<0.05).

FIG. 9: Distributions of short fragment human DNA (short DNA) and long fragment human DNA (long DNA) in stools from patients with normal colonoscopy, large precancerous adenomas, and colorectal cancer. Human DNA quantified by an assay of Alu repeats. Short DNA represents 45 bp fragment amplification products, and long DNA represents 245 bp amplification products.

FIG. 10: Stool distributions of short and long DNA in patients with pancreatic cancer and in healthy controls. Short DNA represents 45 bp fragment amplification products, and long DNA represents 245 bp amplification products.

FIG. 11: Methylated BMP3 gene in stool for detection of colorectal neoplasia. Methylated BMP3 was blindly quantified in stools from patients with colorectal cancers, precancerous adenomas, and normal individuals with real-time methylation-specific PCR. Each circle represents a stool sample.

FIG. 12: Frequency of Specific Base Changes in Colorectal Tumors.

DETAILED DESCRIPTION

This document provides methods and materials related to detecting a neoplasm in a mammal (e.g., a human). For example, this document provides methods and materials for using nucleic acid markers, polypeptide markers, and combinations of markers present in a biological sample (e.g., a stool sample) to detect a neoplasm in a mammal. Such a neoplasm can be a cancer or precancer in the head and neck, lungs and airways, esophagus, stomach, pancreas, bile ducts, small bowel, or colorectum. It will be appreciated that the methods and materials provided herein can be used to detect neoplasm markers in a mammal having a combination of different neoplasms. For example, the methods and materials provided herein can be used to detect nucleic acid and polypeptide markers in a human having lung and stomach neoplasms.

In some cases, the methods and materials provided herein can be used to quantify multiple markers in biological samples (e.g., stool) to yield high sensitivity for detection of lesions (e.g., neoplasms), while preserving high specificity. Such methods can include, for example, a logistic model that adjusts specificity cut-offs based on age, gender, or other variables in a target population to be tested or screened.

In some cases, the methods and materials provided herein can be used to determine whether a mammal (e.g., a human) has colorectal cancer or pancreatic cancer. For example, serotransferin, methylated BMP3, and mutant BRAF markers in stool can be used to identify a mammal as likely having colorectal cancer, while mutant p16, carboxypeptidase B/A, and elastase 2A markers can be used to identify a mammal as likely having pancreatic cancer.

Any suitable method can be used to detect a nucleic acid marker in a mammalian stool sample. For example, such methods can involve isolating DNA from a stool sample, separating out one or more particular DNAs from the total DNA, subjecting the DNAs to bisulfite treatment, and determining whether the separated DNAs are abnormally methylated (e.g., hypermethylated or hypomethylated). In some cases, such methods can involve isolating DNA from a stool sample and determining the presence or absence of DNA having a particular size (e.g., short DNA). It is noted that a single stool sample can be analyzed for one nucleic acid marker or for multiple nucleic acid markers. For example, a stool sample can be analyzed using assays that detect a panel of different nucleic acid markers. In addition, multiple stool samples can be collected from a single mammal and analyzed as described herein.

Nucleic acid can be isolated from a stool sample using, for example, a kit such as the QIAamp DNA Stool Mini Kit (Qiagen Inc., Valencia, Calif.). In addition, nucleic acid can be isolated from a stool sample using the following procedure: (1) homogenizing samples in an excess volume (>1-7 w:v) of a stool stability buffer (0.5M Tris pH 9.0, 150 mM EDTA, 10 mM NaCl) by shaking or mechanical mixing; (2) centrifuging a 10 gram stool equivalent of each sample to remove all particulate matter; (3) adding 1 μL of 100 μg/μL RNase A to the supernatant and incubating at 37° C. for 1 hour, (4) precipitating total nucleic acid with 1/10 volume 3M NaAc and an equal volume isopropanol; and (5) centrifuging and then resuspending the DNA pellet in TE (0.01 M Tris pH 7.4, 0.001 M EDTA). U.S. Pat. Nos. 5,670,325; 5,741,650; 5,928,870, 5,952,178, and 6,020,137 also describe various methods that can be used to prepare and analyze stool samples.

One or more specific nucleic acid fragments can be purified from a nucleic acid preparation using, for example, a modified sequence-specific hybrid capture technique (see, e.g., Ahlquist et al. (2000) Gastroenterology, 119:1219-1227). Such a protocol can involve: (1) adding 300 μL of sample preparation to an equal volume of a 6 M guanidine isothiocyanate solution containing 20 pmol biotinylated oligonucleotides (obtained from, for example, Midland Certified Reagent Co, Midland. Tex.) with sequences specific for the DNA fragments to be analyzed; (2) incubating for two hours at 25° C.; (3) adding streptavidin coated magnetic beads to the solution and incubating for an additional hour at room temperature; (4) washing the bead/hybrid capture complexes four times with IX B+W buffer (1 M NaCl, 0.01 M Tris-HCl pH 7.2, 0.001 M EDTA, 0.1% Tween 20); and (5) eluting the sequence specific captured DNA into 35 μL L-TE (1 mM Tris pH 7.4, 0.1 M EDTA) by heat denaturation of the bead/hybrid capture complexes. Any other suitable technique also can be used to isolate specific nucleic acid fragments.

Nucleic acid can be subjected to bisulfite treatment to convert unmethylated cytosine residues to uracil residues, while leaving any 5-methylctosine residues unchanged. A bisulfite reaction can be performed using, for example, standard techniques: (1) denaturing approximately 1 μg of genomic DNA (the amount of DNA can be less when using micro-dissected DNA specimens) for 15 minutes at 45° C. with 2 N NaOH. (2) incubating with 0.1 M hydroquinone and 3.6 M sodium bisulfite (pH 5.0) at 55° C. for 4-12 hours; (3) purifying the DNA from the reaction mixture using standard (e.g., commercially-available) DNA miniprep columns or other standard techniques for DNA purification; (4) resuspending the purified DNA sample in 55 μL water and adding 5 μl 3 N NaOH for a desulfonation reaction that typically is performed at 40° C. for 5-10 minutes; (5) precipitating the DNA sample with ethanol, washing the DNA, and resuspending the DNA in an appropriate volume of water. Bisulfite conversion of cytosine residues to uracil also can be achieved using other methods (e.g., the CpGenome™ DNA Modification Kit from Serologicals Corp., Norcross, Ga.).

Any appropriate method can be used to determine whether a particular DNA is hypermethylated or hypomethylated. Standard PCR techniques, for example, can be used to determine which residues are methylated, since unmethylated cytosines converted to uracil are replaced by thymidine residues during PCR. PCR reactions can contain, for example, 10 μL of captured DNA that either has or has not been treated with sodium bisulfite, IX PCR buffer, 0.2 mM dNTPs, 0.5 μM sequence specific primers (e.g., primers flanking a CpG island within the captured DNA), and 5 units DNA polymerase (e.g., Amplitaq DNA polymerase from PE Applied Biosystems. Norwalk, Conn.) in a total volume of 50 μl. A typical PCR protocol can include, for example, an initial denaturation step at 94° C. for 5 min, 40 amplification cycles consisting of 1 minute at 94° C., 1 minute at 60° C., and 1 minute at 72° C., and a final extension step at 72° C. for 5 minutes.

To analyze which residues within a captured DNA are methylated, the sequences of PCR products corresponding to samples treated with and without sodium bisulfite can be compared. The sequence from the untreated DNA will reveal the positions of all cytosine residues within the PCR product Cytosines that were methylated will be converted to thymidine residues in the sequence of the bisulfite-treated DNA, while residues that were not methylated will be unaffected by bisulfite treatment.

Purified nucleic acid fragments from a stool sample or samples can be analyzed to determine the presence or absence of one or more somatic mutations. Mutations can be single base changes, short insertion/deletions, or combinations thereof. Methods of analysis can include conventional Sanger based sequencing, pyrosequencing, next generation sequencing, single molecule sequencing, and sequencing by synthesis. In some cases, mutational status can be determined by digital PCR followed by high resolution melting curve analysis. In other cases, allele specific primers or probes in conjunction with amplification methods can be used to detect specific mutations in stool DNA. The mutational signature can comprise not only the event of a base or sequence change in a specific gene, but also the location of the change within the gene, whether it is coding, non-coding, synonymous or non-synonymous, a transversion or transition, and the dinucleotide sequence upstream and downstream from the alteration.

In some cases, a sample can be assessed for the presence or absence of a polypeptide marker. For example, any appropriate method can be used to assess a stool sample for a polypeptide marker indicative of a neoplasm. For example, a stool sample can be used in assays designed to detect one or more polypeptide markers. Appropriate methods such as those described elsewhere (Aebersold and Mann, Nature, 422:198-207 (2003) and McDonald and Yates. Dis. Markers. 18:99-105 (2002)) can be adapted or designed to detect polypeptides in a stool. For example, single-reaction monitoring using a TSQ mass spectrometer can specifically target polypeptides in a stool sample. High resolution instruments like the LTQ-FT or LTQ orbitrap can be used to detect polypeptides present in a stool sample.

The term “increased level” as used herein with respect to the level of an elastase 3A polypeptide is any level that is above a median elastase 3A polypeptide level in a stool sample from a random population of mammals (e.g., a random population of 10, 20, 30, 40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer. Elevated polypeptide levels of an elastase 3A polypeptide can be any level provided that the level is greater than a corresponding reference level. For example, an elevated level of an elastase 3A polypeptide can be 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more fold greater than the reference level of elastase 3A polypeptide in a normal sample. It is noted that a reference level can be any amount. For example, a reference level for an elastase 3A polypeptide can be zero. In some cases, an increased level of an elastase 3A polypeptide can be any detectable level of an elastase 3A polypeptide in a stool sample.

The term “increased level” as used herein with respect to the level of an carboxypeptidase B polypeptide level is any level that is above a median carboxypeptidase B polypeptide level in a stool sample from a random population of mammals (e.g., a random population of 10, 20, 30, 40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer. Elevated polypeptide levels of carboxypeptidase B polypeptide can be any level provided that the level is greater than a corresponding reference level. For example, an elevated level of carboxypeptidase B polypeptide can be 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more fold greater than the reference level carboxypeptidase B polypeptide observed in a normal stool sample. It is noted that a reference level can be any amount. For example, a reference level for a carboxypeptidase B polypeptide can be zero. In some cases, an increased level of a carboxypeptidase B polypeptide can be any detectable level of a carboxypeptidase B polypeptide in a stool sample.

The term “increased level” as used herein with respect to the level of DNA fragments less than about 200 or less than about 70 base pairs in length is any level that is above a median level of DNA fragments less than about 200 or less than about 70 base pairs in length in a stool sample from a random population of mammals (e.g., a random population of 10, 20, 30, 40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer. In some cases, an increased level of DNA fragments less than about 200 or less than about 70 base pairs in length can be any detectable level of DNA fragments less than about 200 or less than about 70 base pairs in length in a stool sample.

The term “elevated methylation” as used herein with respect to the methylation status of a BMP3 or ALX nucleic acid is any methylation level that is above a median methylation level in a stool sample from a random population of mammals (e.g., a random population of 10, 20, 30, 40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer. Elevated levels of BMP3 or ALX methylation can be any level provided that the level is greater than a corresponding reference level. For example, an elevated level of BMP3 or ALX methylation can be 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more fold greater than the reference level methylation observed in a normal stool sample. It is noted that a reference level can be any amount.

The term “elevated mutation score” as used herein with respect to detected mutations in a matrix panel of particular nucleic acid markers is any mutation score that is above a median mutation score in a stool sample from a random population of mammals (e.g., a random population of 10, 20, 30, 40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer. An elevated mutation score in a matrix panel of particular nucleic acid markers can be any score provided that the score is greater than a corresponding reference score. For example, an elevated score of K-ras or APC mutations can be 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more fold greater than the reference score of K-ras or APC mutations observed in a normal stool sample. It is noted that a reference score can be any amount.

In some cases, a ratio of particular polypeptide markers can be determined and used to identify a mammal having an aero-digestive cancer (e.g., a colorectal cancer or a pancreatic cancer). For example, a ratio provided herein (e.g., the ratio of carboxypeptidase B polypeptide levels to carboxypeptidase A2 polypeptide levels) to can be used as described herein to identify a mammal having a particular neoplasm (e.g., pancreatic cancer).

In some cases, a matrix marker panel can be used to identify mammals having an aero-digestive cancer (e.g., a colorectal cancer or a pancreatic cancer). In some cases, such panel also can identify the location of the aero-digestive cancer. Such a panel can include nucleic acid markers, polypeptide markers, and combinations thereof and can provide information about a mutated marker gene, the mutated region of the marker gene, and/or type of mutation. For example, data can be analyzed using a statistical model to predict tumor site (e.g., anatomical location or tissue of origin) based on inputs from sequencing data (such as by specific nucleic acid or combination of nucleic acids mutated, specific mutational location on a nucleic acid, and nature of mutation (e.g. insertion, deletion, transition, or transversion) or by any combination thereof) and/or data from polypeptide or other types of markers. For example, a Site of Tumor Estimate (SITE) model can be used to predict tumor site using a matrix panel of markers that are present to variable extent across tumors.

In some cases, data can be analyzed using quantified markers to create a logistic model, which can have both high sensitivity and high specificity. For example, a logistic model can also incorporate population variables like gender and age to adjust cut-off levels for test positivity and thereby optimize assay performance in a screening setting. In some cases, a Quantitative Logistic to Enhance Accurate Detection (Q-LEAD) Model can be used with any marker class or combination of markers as long as they can be quantified.

This document also provides methods and materials to assist medical or research professionals in determining whether or not a mammal has an aero-digestive cancer. Medical professionals can be, for example, doctors, nurses, medical laboratory technologists, and pharmacists. Research professionals can be, for example, principle investigators, research technicians, postdoctoral trainees, and graduate students. A professional can be assisted by (1) determining the ratio of particular polypeptide markers in a stool sample, and (2) communicating information about the ratio to that professional, for example. In some cases, a professional can be assisted by (1) determining the level of human DNA, the methylation status of genes such as BMP3, and the mutation score of genes such as APC and K-ras, and (2) communicating information about the level of DNA, the methylation status of particular genes, and the mutation score of particular genes to the professional. In some cases, a professional can be assisted by (1) detecting mutations in cancer-related genes such as K-ras, p53, APC, p16. EGFR. CTNNB1. BRAF, and SMAD4, as a matrix marker panel, and (2) communicating information regarding the mutations to the professional.

After the ratio of particular polypeptide markers, or presence of particular nucleic acid markers in a stool sample is reported, a medical professional can take one or more actions that can affect patient care. For example, a medical professional can record the results in a patient's medical record. In some cases, a medical professional can record a diagnosis of an aero-digestive cancer, or otherwise transform the patient's medical record, to reflect the patient's medical condition. In some cases, a medical professional can review and evaluate a patient's entire medical record, and assess multiple treatment strategies, for clinical intervention of a patient's condition. In some cases, a medical professional can record a tumor site prediction with the reported mutations. In some cases, a medical professional can request a determination of the ratio of particular polypeptide markers to predict tumor site. In some cases, a medical professional can review and evaluate a patient's entire medical record and assess multiple treatment strategies, for clinical intervention of a patient's condition.

A medical professional can initiate or modify treatment of an aero-digestive cancer after receiving information regarding a ratio of particular polypeptide markers or the presence of nucleic acid markers in a patient's stool sample. In some cases, a medical professional can compare previous reports and the recently communicated ratio of particular polypeptide markers, or presence of nucleic acid markers, and recommend a change in therapy. In some cases, a medical professional can enroll a patient in a clinical trial for novel therapeutic intervention of an aero-digestive cancer. In some cases, a medical professional can elect waiting to begin therapy until the patient's symptoms require clinical intervention.

A medical professional can communicate the ratio of particular poly peptide markers to a patient or a patient's family. In some cases, a medical professional can provide a patient and/or a patient's family with information regarding aero-digestive cancers, including treatment options, prognosis, and referrals to specialists, e.g., oncologists and/or radiologists. In some cases, a medical professional can provide a copy of a patient's medical records to communicate the ratio of particular polypeptide markers to a specialist.

A research professional can apply information regarding a subject's ratio of particular polypeptide markers to advance aero-digestive cancer research. For example, a researcher can compile data on the ratio of particular polypeptide markers, and/or presence of particular nucleic acid markers, with information regarding the efficacy of a drug for treatment of aero-digestive cancer to identify an effective treatment. In some cases, a research professional can obtain a subject's ratio of particular polypeptide markers, and/or determine the presence of particular nucleic acid markers to evaluate a subject's enrollment, or continued participation in a research study or clinical trial. In some cases, a research professional can classify the severity of a subject's condition, based on the ratio of particular polypeptide markers and/or the levels of particular nucleic acid markers. In some cases, a research professional can communicate a subject's ratio of particular polypeptide markers, and/or the presence of particular nucleic acid markers to a medical professional. In some cases, a research professional can refer a subject to a medical professional for clinical assessment of an aero-digestive cancer, and treatment of an aero-digestive cancer.

Any appropriate method can be used to communicate information to another person (e.g., a professional). For example, information can be given directly or indirectly to a professional. For example, a laboratory technician can input the ratio of particular polypeptide markers and/or particular nucleic acid markers into a computer-based record. In some cases, information is communicated by making a physical alteration to medical or research records. For example, a medical professional can make a permanent notation or flag a medical record for communicating a diagnosis to other medical professionals reviewing the record. In addition, any type of communication can be used to communicate the information. For example, mail, e-mail, telephone, and face-to-face interactions can be used. The information also can be communicated to a professional by making that information electronically available to the professional. For example, the information can be communicated to a professional by placing the information on a computer database such that the professional can access the information. In addition, the information can be communicated to a hospital, clinic, or research facility serving as an agent for the professional.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES Example 1—Multi-Marker Quantitation and a Q-LEAD Model

Most approaches at marker detection in stool have been qualitative. When such qualitative approaches are applied to assay of multiple markers (targeting multiple markers is required with neoplasm detection due molecular heterogeneity), sensitivity is achieved at the expense of compounded non-specificity Non-specificity can lead to prohibitive programmatic cost with population screening due to the expensive and unnecessary evaluations of false-positive tests. However, if markers are quantified, then a logistic model can be created to achieve both high sensitivity and high specificity. Such a logistic model can also incorporate population variables like gender and age to adjust cut-off levels for test positivity and thereby optimize assay performance in a screening setting (FIG. 1). This Quantitative Logistic to Enhance Accurate Detection (Q-LEAD) Model can be used with any marker class or combination of markers as long as they can be quantified.

A combination of more than one marker was undertaken to achieve the desired sensitivity and specificity for cancer detection. Binary regression methods predicting disease as a function of diagnostic tests estimate the optimal combination of the tests for classifying a subject as diseased or not. McIntosh and Pepe, Biometrics 58: 657-664 (2002). A logistic regression model can assess the relationship between a binary dependent response variable such as presence or absence of disease and one or more independent predictor variables. The independent predictors may be qualitative (e.g., binary) or quantitative (e.g., a continuous endpoint). In the Q-LEAD model, the independent predictors can include such biological markers as K-ras, BMP3 and DNA concentration, and others. Importantly, the model incorporates the demographic variables of gender and age, as we have observed that both age and gender influence molecular marker levels in stool. As average stool marker levels increase with age and male gender, failure to adjust for these variables would yield suboptimal specificity in men and elderly persons tested. Coefficients are estimated from the sample data for each term in the model. The result of the model is a risk score for each subject. Cutoffs for predicting disease state from this risk score can be determined in order to maximize sensitivity and specificity of the marker combinations for predicting disease as desired. The inclusion of demographic variables allows these cutoffs to be determined as a function of age and gender.

As an application of the Q-LEAD Model, the following was performed to evaluate a quantitative stool DNA assay approach targeting three informative markers for the detection of colorectal neoplasia. Subjects included 34 with colorectal cancer, 20 with adenomas >1 cm, and 26 with normal colonoscopy. Subjects added a DNA stabilization buffer with stool collection, and stools were frozen at −80° C. within 72 hours. From thawed stool aliquots, crude DNA was extracted by standard methods, and target genes were enriched by sequence capture. K-ras mutation score, methylation of BMP3 gene, and concentration of human DNA (245 bp length) were respectively quantified by a digital melt curve assay, real-time methylation-specific PCR, and real-time Alu PCR, respectively. Assays were performed blinded. A logistic model, which incorporates three markers and gender, was constructed to analyze discrimination by combined markers.

Age medians were 60 for patients with colorectal cancer, 66 for those with adenomas, and 61 for normal controls; and male/female distributions were 23/11, 9/11, and 10/16, respectively. Detection rates of colorectal neoplasms were determined by individual quantitative markers at specificity cut-offs of 96 percent and by combined markers (Table 1). Discrimination by combined markers was calculated using a qualitative binomial method (each marker considered as positive or negative based on individual 96 percent specificity) and by the Q-LEAD model (sensitivity data shown at overall specificity of 96 percent).

TABLE 1 Specificity and sensitivity of cancer markers. Sensitivity Specificity Cancers Adenomas Both Individual Markers k-ras mutation 96% 42% 32% 38% BMP3 methylation 96% 45% 32% 40% DNA concentration 96% 65% 40% 56% Combined Markers Qualitative method 88% 90% 58% 78% Q-LEAD Model 96% 90% 47% 76%

By quantitative assay and multivariable analysis of an informative marker panel, stool DNA testing can achieve high sensitivity while preserving high specificity for detection of colorectal neoplasia. The particular three-marker combination of mutant K-ras. BMP3 methylation, and human DNA concentration represents a complementary, high-yield panel.

The above data set and additional data were analyzed as follows. A quantitative stool DNA assay approach targeting four informative markers for use in the detection of colorectal cancer and advanced adenoma was evaluated. Subjects comprised 74 patients with colorectal cancer, 27 with an adenoma >1 cm, and 100 with normal colonoscopy. Stools were collected with a stabilization buffer before or >1 week after colonoscopy and were frozen at −80° C. within 24 hours of collection. From thawed stool aliquots, crude DNA was extracted as described above, and target genes were enriched by sequence-specific capture. Human DNA concentration, K-ras and APC mutation scores, and BMP3 methylation were sensitively quantified by real-time Alu PCR, a digital melt curve assay (Zou et al., Gastroenterology, “High Detection Rates of Colorectal Neoplasia by Stool DNA Testing With a Novel Digital Melt Curve Assay.” (2008)), and real-time methylation-specific PCR, respectively. Assays were performed blindly. Sensitivities and specificities of single makers and their combinations were analyzed.

Age medians were 61 for patients with colorectal cancer, 67 for those with adenomas, and 59 for normal controls; and, male/female ratios were 52/22, 15/12, and 37/63, respectively. The table displays detection rates of colorectal neoplasms by individual quantitative markers at specificities of 90% and by combined markers at two specificities (Table 2) Data in this table represent a training set and have not been adjusted for age and gender. Yet, it is clear that the full panel of Alu, K-ras, APC, and BMP3 detected more neoplasms than any individual marker, p<0.05 At 90% specificity, the full panel detects more adenomas >3 cm (90%, 9/10) than <3 cm (47%, 8/17), p<0.05, and more colorectal cancers at stages III-IV (89%, 40/45) than at stages I-II (69%, 20/29) p<0.05. Neoplasm detection rates were not affected by tumor location.

TABLE 2 Specificity and sensitivity of a four marker panel Sensitivity Colorectal cancers Adenomas Specificity I-II III-IV ≦3 cm >3 cm Individual Markers APC mutation 90% 38% 40% 47% 50% k-ras mutation 90% 46% 42% 24% 50% BMP3 methylation 90% 36% 38% 12% 30% DNA concentration 90% 52% 76% 41% 50% Combined Markers 90% 69% 89% 47% 90%

In conclusion, a quantitative stool DNA assay system that incorporates a stabilization buffer with specimen collection, high analytical sensitivity, and a panel of broadly informative markers can achieve high detection rates of both colorectal cancers and advanced adenoma.

Example 2—SITE Model and Matrix Marker Panel

A statistical model (Site of Tumor Estimate (SITE)) can be used to predict tumor site (e.g., anatomical location or tissue of origin) based on inputs from sequencing data (such as by specific nucleic acid or combination of nucleic acids mutated, specific mutational location on a nucleic acid, and nature of mutation (e.g. insertion, deletion, transition, or transversion) or by any combination thereof) and/or data from polypeptide or other types of markers.

A matrix marker panel was developed to include eight cancer-related genes: K-ras, p53. APC, p16, EGFR, CTNNB1, BRAF, and SMAD4. The mutation frequencies of these genes were tabulated against the six major aero-digestive cancers based on literature or public database reviews and on actual sequencing observations (Table 3). Literature frequencies were derived from the COSMIC somatic mutation database, review articles, and texts.

TABLE 3 Matrix Panel of Markers by Tumor Site AD Cancer Site N p16 p53 K-ras APC SMAD4 EGFR CTNNB1 BRAF Total Unique Literature Colorectal <5% 50-75% 40% 85% 14% NA 13%  20% Pancreatic 85-100%     50-60% 80-90%    10-40%    30% NA 3-8%  <5% Lung 15-25%    25-75% 20-40%     5%  7% 30% 6% <5% Bile Duct 15-60%    30-60% 40% 30-40%    17% NA 1% 14% Gastric 5-30%  20-50% 10% 20-60%    NA <1% 30-50%     <5% Esophageal 5-90%  40-90% 5-12%  5-60%  NA NA 1% <5% Actual (non-dbSNP) Colorectal 57  5%    47% 26% 75% 25% 12% 2% 30% 98% Pancreatic 24 29%    17% 62% 54%  8%  8% 4%  8% 83% Lung 56  9%    57%  9% 16% 14% 14% 2%  4% 77% Bile Duct 15 13%    27% 13% 20% 13% 0 0  7% 67% Gastric 23 17%    22%  4% 35% 17%  4% 4% 0 65% Esophageal 24  4%    46%  4% 33% 17%  4% 0  4% 79%

Some of the frequencies include other genetic alterations than simply single base changes and small insertions/deletions such as methylation events, large homozygous deletions, and copy number changes. Such alterations would not be reflected in the actual frequency table. Actual frequencies were derived by sequencing coding and flanking gene regions from 245 patient tissue samples reflecting the spectrum of aero-digestive cancers. Only non-synonymous and splice site alterations were tabulated. When specific mutational hot-spot sites were able to be identified for particular genes, only those sites were analyzed.

The matrix panel includes markers that are present to variable extent across these tumors so that their aggregate use achieves high overall sensitivity and allows prediction of tumor site using the SITE Model. 70% of tumors harbored one or more mutations from the eight gene panel. Some gene mutations, like those associated with p16, are common in tumors above the colon but are rare for those in the colon. Mutant K-ras is frequent with colorectal and pancreatic cancers but infrequent in the other cancers. Mutations in EGFR clustered with lung and colorectal tumors and mutations in SMAD4 clustered with stomach and colorectal tumors. Genes such as p53, are commonly mutated across many different types of cancers, but specific mutational locations or types of mutations within p53 and other genes differ between tumor site (e.g., Greenman et al, Nature, 446(7132)-153-8 (2007), Soussi and Lozano, Biochem. Biophys. Res. Commun., 331(3):834-42 (2005); Stephens et al., Nat. Genet., 37(6):590-2 (2005); Sjoblom et al., Science, 314(5797):268-74 (2006); Wood et al., Science, 318(5853): 1108-13 (2007), and Davies, Cancer Res., 65(17):7591-5(2005)) and can be factored in to the SITE Model to predict tumor site. Single base substitutions were the most common type of mutation throughout the panel and those that predicted colorectal tumors included C-G and A-T transversions (FIG. 12). Other tumor sites had similarly unique base change profiles. (Table 4). Insertion/deletions mutations were most common with colorectal tumors, particularly adenomas.

TABLE 4 Specific Base Change Fractions in AD Tumors C > T T > C G > C A > T G > T T > G Tumor (G > A) (A > G) (C > G) (T > A) (C > A) (A > C) Head and Neck 0.38 0.12 0.38 0.12 Esophageal 0.8 0.07 0.13 Lung 0.3 0.11 0.02 0.13 0.34 0.09 Stomach 0.5 0.25 0.17 0.08 Pancreas 0.41 0.15 0.04 0.07 0.33 Bile Duct 0.5 0.12 0.25 0.12 CRA 0.34 0.05 0.11 0.11 0.34 0.03 CRC 0.4 0.05 0.02 0.24 0.26 0.03

Polypeptide markers found in stool, such as by proteomic approaches, can also be used to detect aero-digestive neoplasms and predict tumor site. The following was performed to identify and explore candidate polypeptide markers in stool for the discriminate detection of pancreatic cancer. Subjects included 16 cases with pancreatic cancer, 10 disease controls (colorectal cancer), and 24 healthy controls. Whole stools were collected and frozen promptly in aliquots at −80° C. Thawed aliquots were centrifuged, and the aqueous supernatant from each was analyzed. Polypeptides were separated by I-D electrophoresis, excised from gels, and digested for mass spectrometric analysis using an LTQ-Orbitrap. Data outputs were searched using Mascot. Sequest, and X! Tandem programs against an updated

Swissprot database that included all cataloged species. Unique peptide counts and ratio calculations were performed using Scaffold software.

Median age for pancreatic cancer cases was 67, for colorectal cancer controls 63, and for healthy controls 62; and male/female distributions were 9/7, 6/4, and 9/15, respectively. Using shotgun-proteomic techniques on stools, two pancreatic enzymes (carboxypeptidases B and A2) were conspicuous, as unique spectral counts of the former were commonly elevated with pancreatic cancer and of the latter commonly decreased. Considered together as the ratio of carboxypeptidase B/carboxypeptidase A2, pancreatic cancer cases were almost completely separated from colorectal cancer and healthy control groups. Median ratios were 0.9, 0.2, and 0.3, respectively. At a specificity cut-off for the carboxypeptidase B/A2 ratio at 100% (i e, ratios from normal control and colorectal cancer stools all below cut-off), sensitivity for pancreatic cancer was 86 percent (FIG. 2). Only two pancreatic cancers were misclassified.

These results demonstrate that a stool assay of polypeptide markers can be a feasible non-invasive approach to the detection pancreatic cancer. These results also demonstrate that multivariable analysis of specific polypeptide ratios can be used.

In addition, polypeptide markers unique to colorectal neoplasms were identified (Table 5). For example, serotransferrin was found in stools from patients with colorectal cancer but not in those with pancreatic cancer. These markers when considered as part of a matrix panel contribute both to overall sensitivity for tumor detection and help discriminate colorectal from pancreatic cancer.

TABLE 5 Positive Stool Findings. Carboxypeptidase B/A2* Serotransferrin Colorectal Cancer 0 60% Pancreatic Cancer 86% 0 Normal controls 0 0 *ratio >0.75 considered positive

Another polypeptide in stool that is pancreatic cancer specific is elastase 3A. Methods and Results demonstrating this are as follows:

Stool Preparation

Samples were collected in phosphate buffered saline and either dropped off in clinic or mailed in collection tub. Samples were homogenized and frozen within 72 hours after receipt. Frozen stools were diluted 1:3 w:v in PBS (Roche, Cat#1666789). Diluted stools were stomached in a filter bag (Brinkman, BA6041/STR 177×305 mm) for 60 seconds on control setting and spun at 10.000 rpms for 30 minutes. Following an additional 10 minute spin at 14,000 rpm, the supernatant was filtered through a 0.45-μm syringe filter and analyzed. Total protein present in stool was quantitated using a Bradford Protein Assay kit (Pierce).

1-Dimensional Electrophorests

Stool supernatants were diluted 1:1 in Leammli-BME buffer and run on a 10.5-14% gradient gel. Vertical slices were cut from 250 kDa to 15 kDa and in-gel digested using methods described elsewhere (e.g., Wilm et al., Nature, 379:466-469 (1996)). Bands were destained, dehydrated, digested in trypsin, extracted, and lyophilized for MS analysis.

Mass Spectrometry

Lyophilized samples were reconstituted and injected with a flow of 500 nL/min and a 75 minute gradient from 5-90% 98% acetonitrile. MS was performed in data dependent mode to switch automatically between MS and MS² acquisition on the three most abundant ions. Survey scans were acquired with resolution r=60,000 at 40 m/z using FWHM with a target accumulation of 10⁶ counts. An isolation width of 2.5 m/z was applied. Exclusion mass width was 0.6 m/z on low end and 1.5 m/x on high end. All acquisition and method development was performed using Xcaliber version 2.0.

Database Searching all Ms/Ms

Samples were analyzed using Mascot (Matrix Science, London, UK; version 2.1.03), Sequest (ThermoFinnigan. San Jose, Calif.; version 27, rev. 12) and X! Tandem (World Wide Web at “thegpm.org”; version 2006.09.15.3). Mascot and X! Tandem were searched with a fragment ion mass tolerance of 0.80 Da and a parent ion tolerance of 10.0 PPM. Sequest was searched with a fragment ion mass tolerance of 1.00 Da. Nitration of tyrosine was specified in Mascot as a variable modification.

Criteria for Polypeptide Identification

Scaffold (version Scaffold-01_06_06, Proteome Software Inc., Portland. Oreg.) was used to validate MS/MS based polypeptide identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (Keller et al. Anal. Chem., 74(20):5383-92 (2002)). Polypeptide identifications were accepted if they could be established at greater than 99.0 percent probability and contained at least two identified peptides. Polypeptide probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, Anal. Chem., 75(17):4646-58 (2003)). Polypeptides that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.

Specific to Elastase SA Ratio Determination

Ratios of elastase 3A were determined using spectral counts for each polypeptide. Ratios were determined by dividing the number of unique peptides of elastase 3A (determined using a composite ID from database search modules Mascot, XTandem, and Seaquest and compiled in Scaffold) by the number of unique peptides from another polypeptide such as pancreatic alpha-amylase.

Results

The concentration of a specific pancreatic enzyme, elastase 3A, was consistently found to be elevated in the fecal supernatant of patients with pancreatic cancer as compared to normal controls or patients with non-pancreatic cancer (FIG. 3). These finding indicate that fecal concentration of elastase 3A is an accurate marker for pancreatic cancer. In addition, the ratio of elastase 3A against other pancreatic enzymes (or other stable fecal polypeptides) was found to be especially discriminant for pancreatic cancer and obviates the need to determine absolute elastase 3A concentrations (FIG. 4). While mass spectrometry was used to make these observations, elastase 3A levels and ratios including elastase 3A can be measured using other methods as well.

Example 3—Digital Melt Curve Assay for Scanning Mutations

A sensitive, rapid, and affordable method for scanning mutations in bodily fluids at high-throughput was developed. A melt curve assay is a post-PCR technique that can be used to scan for mutations in PCR amplicons. Mutations in PCR products can be detected by changes in the shape of the melting curve (heterozygote from mutant sample) compared to a reference sample (homozygote from wild-type sample) (FIG. 5). Melt curve assay can scan all mutations in a DNA fragment <400 bp in less than 10 minutes, rather than individually targeting single mutations. Regular melt curve assays can detect mutations down to a limit of 5% mutant:wild-type and, thus, are not sensitive enough to detect mutations in many biological samples. For instance, in stool, an analytical sensitivity of 1% or less is required in order to detect precancerous polyps or small early stage cancers. Importantly, a quantitative score can be given to density of target mutations (FIG. 6).

Digital PCR can augment the sensitivity of PCR to detect low abundance mutations. Gene copies can be diluted and distributed into 96 wells of a plate to increase the percentage of mutant copy to wild-type copies in certain wells. For example, if a stool DNA sample contains only 1% of mutant BRAF copies compared to wild-type copies, distributing 300 copies of BRAF gene into a 96-well plate can lead to three wells with an average mutant ratio of 33 percent (1:3). After PCR amplification, these three wells with mutant copies can be detected by sequencing or other approaches. Since digital PCR requires PCR on a whole 96-well plate and 96 sequencings (or other approaches) for each target, it can be slow and costly.

The concept of digital melt curve assay is to combine the scanning ability and speed of high resolution melt curve assay with the sensitivity of digital PCR. Miniaturizing and automating this technology dramatically lowers per assay cost and achieves high-throughput necessary for population screening.

The following procedure was used to perform a digital melt curve assay. To prepare a DNA sample, gene target fragments (e.g., BRAF. K-ras. APC, p16, etc.) were captured from stool DNA using a sequence-specific capture method and were quantified with real-time PCR. About 200 to 2000 gene copies were mixed in tube with all PCR reagents. An average of 2 to 20 copies (variable) were distributed to each well of a 96-well plate. PCR amplification was performed using specific primers on the plate (e.g., one target per plate). Final concentrations of PCR mastermix for Digital Melt Curve assays in a 96-well plate (500 L dispersed to 96 wells with each well containing 5 μL) were as follows: 2× pfx amplification buffer (Invitrogen), 0.3 mM each dNTP, 200 nM forward primer, 200 nM reverse primer, 1 mM MgSO₄, 0.02 unit/μL Platinum® pfx polymerase (Invitrogen), and 0.1 unit/μL LcGreen+ dye (Idaho Tech). A high resolution melt curve assay was used to identify the wells with mutant copies. Sequencing was optionally performed to confirm 1 to 2 representative wells.

In some cases, emulsion PCR can be used in place of digital PCR. In such cases, each lipid drop can become a tiny PCR reactor of one single molecule of gene.

TABLE 6 Sequence Specific Capture Probes and DNA Primer Capture Target Probe/ SEQ Gene Region Primer Oligo Sequence (5′→3′) ID No. KRAS Condons 12/13 Probe GTGGACGAATATGATCCAACAATAGAGGTAAATCTTG  1 Condons 12/13 Primer 1 AGGCCTGCTGAAAATGACTG  2 TTGTTGGATCATATTCGTCCAC  3 Condons 12/13 Primer 2 TAAGGCCTGCTGAAAATGAC  4 ATCAAAGAATGGTCCTGCAC  5 Condons 12/13 Primer 3 CGTCTGCAGTCAACTGGAATTT  6 TGTATCGTCAAGGCACTCTTGC  7 Condons 12/13 Primer 4 CTTAAGCGTCGATGGAGGAG  8 TTGTTGGATCATATTCGTCCAC  3 BRAF V600E Probe CCAGACAACTGTTCAAACTGATGGGACCCACTCCATC  9 V600E Primer CCACAAAATGGATCCAGACA 10 TGCTTGCTCTGATAGGAAAATG 11 APC MCR Probe 1 CAGATAGCCCTGGACAAACCATGCCACCAAGCAGAAG 12 MCR Probe 2 TTCCAGCAGTGTCACAGCACCCTAGAACCAAATCCAG 13 MCR Probe 3 ATGACAATGGGAATGAAACAGAATCAGAGCAGCCTAAAG 14 Condons 1286-1346 Primer 1 TTCATTATCATCTTTGTCATCAGC 15 CGCTCCTGAAGAAAATTCAA 16 Condons 1346-1367 Primer 2 TGCAGGGTTCTAGTTTATCTTCA 17 CTGGCAATCGAACGACTCTC 18 Condons 1394-1480 Primer 3 CAGGAGACCCCACTCATGTT 19 TGGCAAAATGTAATAAAGTATCAGC 20 Condons 1450-1489 Primer 4 CATGCCACCAAGCAGAAGTA 21 CACTCAGGCTGGATGAACAA 22 Condon 1554 Primers GAGCCTCGATGAGCCATTTA 23 TCAATATCATCATCATCTGAATCATC 24 102457delC Primer 6 GTGAACCATGCAGTGGAATG 25 ACTTCTCGCTTGGTTTGAGC 26 102457delC Primer 7 CAGGAGACCCCACTCATGTT 19 CATGGTTTGTCCAGGGCTAT 27 102457delC Primer 8 GTGAACCATGCAGTGGAATG 25 AGCATCTGGAAGAACCTGGA 28 TP53 Exon 4 Probe AAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAGA 29 Exon 4 Primer CCCTTCCCAGAAAACCTACC 30 GCCAGGCATTGAAGTCTCAT 31 Exon 5 Probe CATGGCCATCTACAAGCAGTCACAGCACATGACGGAG 32 Exon 5 Primer CACTTGTGCCCTGACTTTCA 33 AACCAGCCCTGTCGTCTCT 34 Exon 6 Probe AGTGGAAGGAAATTTGCGTGTGGAGTATTTGGATGAC 35 Exon 6 Primer CAGGCCTCTGATTCCTCACT 36 CTTAACCCCTCCTCCCAGAG 37 Exon 7 Probe ATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGA 38 Exon 7 Primer CTTGGGCCTGTGTTATCTCC 39 GGGTCAGAGGCAAGCAGA 40 Exon 8 Probe CGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGC 41 Exon 8 Primer GGGAGTAGATGGAGCCTGGT 42 GCTTCTTGTCCTGCTTGCTT 43

Example 4—Sensitive Detection of Mutations Using a Digital Melt Curve Assay

The following was performed to develop a quantitative method for scanning gene mutations and to evaluate the sensitivity of the quantitative method for detecting target mutations in stool. A digital melt curve assay was designed by combining digital PCR to a modified melt curve assay. Target genes in low concentration were PCR amplified with a saturated DNA dye, LcGreen+, in a 96-well plate. Each well contained a small number of gene copies, which allowed high mutation/wild-type ratios in some wells that were then detected by melt curve scanning using a LightScanner. Mutations were scored based on the number of wells containing mutant copies in a 96-well plate. To test sensitivity, mutant genes were spiked into a wild-type pool at 0.1, 0.5, 1, 5, and 10% dilutions, and analyzed using digital melt curve assay with 250-1000 gene copies per 96-well plate. This method was then applied in the stool detection of APC, p53, K-ras, and BRAF mutations from 48 patients known to have mutations in one of these genes in matched tumor tissue. Subjects included 9 patients with pancreatic cancer, 31 with colorectal cancer, and 8 with colorectal adenoma >1 cm. All mutations detected by digital melt curve were further confirmed by Sanger sequencing.

The digital melt curve assay detected as few as 0.1% mutant copies for amplicons <350 bp using one 96-well plate (FIG. 7), compared to the detection limit for regular melt curve of ≧5 percent. Each mutation scanning took 8-10 minutes with this manual approach. Mutations of APC, p53. K-ras, and BRAF genes were all successfully scanned with digital melt curve in quantitative fashion. Tissue-confirmed mutations were detected from matched stools in 88 percent (42148) of patients with gastrointestinal neoplasms, including 89 percent with pancreatic cancer, 90 percent with colorectal cancer, and 75 percent with colorectal adenoma >1 cm.

These results demonstrate that a digital melt curve assay can be a highly sensitive approach for detecting mutations in stool, and that it has potential for diagnostic application with both upper and lower gastrointestinal neoplasms.

Example 5—Using a Digital Melt Curve Assay to Detect Adenomas

Archived stools were used to evaluate a digital melt curve assay of DNA markers for detection of advanced adenomas and to compare the accuracy of the digital melt curve assay with that of occult blood testing and a commercial DNA marker assay method (EXACT Sciences). Average risk subjects collected stools without a preservative buffer and mailed them to central processing laboratories for banking and blinded stool testing by Hemoccult. HemoccultSENSA, and DNA marker assay. All subjects underwent a colonoscopy, and tissue from advanced adenomas was archived. Archival stools were selected from the 27 patients with a colorectal adenoma >1 cm found to harbor mutant K-ras on tissue analyses and from the first 25 age and gender matched subjects with normal colonoscopy. Standard methods were used to extract crude DNA from fecal aliquots, and K-ras gene was enriched by sequence capture. Mutations in the K-ras gene were quantified by a digital melt curve assay based on the number of wells containing mutant gene copies in a 96-well plate and confirmed by sequencing.

Median age with adenomas was 67 and controls 71; and males/females were 12/15 and 13/14, respectively. Median adenoma site was 1.5 cm (range 1-3 cm). Based on a cut-off of >3 wells with mutant K-ras, the digital melt curve assay yielded an overall sensitivity of 59 percent for adenomas with a specificity of 92 percent; sensitivity for adenomas >2 cm was 80 percent (8/10) and for those <2 cm was 47 percent (8/17), p=0.1. In these same stools, overall adenoma detection rates were 7 percent by Hemoccult, 15 percent by HemoccultSENSA, and 26 percent by the EXACT Sciences K-ras assay (p<0.05 for each vs. digital melt curve) (FIG. 8). Respective specificities were 92 percent, 92 percent, and 100 percent.

These results demonstrate that an analytically-sensitive digital melt curve assay method can be used to detect a majority of advanced colorectal adenomas and improve yield over current stool test approaches.

Example 6—Short DNA as a Cancer Marker

Free human DNA is present in all human stools and arises from cells shed (exfoliated) from the normal surface (mucosa) of the aero-digestive tract (mouth/throat, lungs, and all digestive organs) and from tumors or other lesions that may be present. It has been generally accepted that “long DNA” in stool reflects that presence of colorectal and other aero-digestive tumors, in that cells exfoliated from cancers do not undergo typical cell death (apoptosis) which would shorten DNA. Specifically, because DNA from apoptotic cells would be broken down to fragment lengths shorter than 100 bp, long DNA was defined as being longer than 100 bp. Indeed, levels of long DNA were elevated in stools from patients with colorectal and other cancers as compared to those from healthy controls (Zou et al., Cancer Epidemiol. Biomarkers Prev., 15: 115 (2006): Ahlquist et al., Gastroenterology, 119:1219 (2000); and Boynton et al. Clin. Chem., 49:1058 (2003)). As such, long DNA in stool can serve as a marker for colorectal and other tumors.

“Short DNA” (i.e., <100 bp in length), however, was found to be as or more discriminant than long DNA as a tumor marker in stool for detection of both colorectal (FIG. 9) and pancreatic (FIG. 10) neoplasia.

Briefly, methods and materials similar to those described elsewhere were used to detect short DNA present in stool samples (Zou et al., Cancer Epidemiol, Biomarkers Prev., 15(6): 1115 (2006)). Total DNA was extracted by isopropanol precipitation from 19 blinded stool samples, 9 pancreatic adenocarcinoma, and 10 age/gender matched normals. The DNA pellets were taken up in 8 mL of 10-fold diluted TE, pH 8. The Alu sequence consists of conserved regions and variable regions. In the putative consensus Alu sequence, the conserved regions are the 25-bp span between nucleotide positions 23 and 47 and the 16-bp span between nucleotide positions 245 and 260. Although primers can be designed in any part of the Alu sequences, for more effectively amplifying Alu sequences, the PCR primers are preferably completely or partially (at least the 3′-regions of the primers) located in the conserved regions. Primers specific for the human Alu sequences were used to amplify fragments of differing lengths inside Alu repeats. The sequences were as follows:

Amplicon size Primer Sequences 245 bp Forward Primer: 5′-ACGCCTGTAATCCCAGCACTT-3′ (SEQ ID NO: 44) Reverse Primer: 5′-TCGCCCAGGCTGGAGTGCA-3′ (SEQ ID NO: 45) 130 bp Forward Primer: 5′-TGGTGAAACCCCGTCTCTAC-3′ (SEQ ID NO: 46) Reverse Primer: 5′-CTCACTGCAACCTCCACCTC-3′ (SEQ ID NO: 47)  45 bp Forward Primer: 5′-TGGTGAAACCCCGTCTCTAC-3′ (SEQ ID NO: 46) Reverse Primer: 5′-CGCccGGCTAATTTTTGTAT-3′ (SEQ ID No: 48)

Stool DNA was diluted 1:5 with Ix Tris-EDTA buffer (pH 7.5) for PCR amplification. Tris-EDTA buffer-diluted stool DNA (1 μL) was amplified in a total volume of 25 μL containing Ix iQ SYBR Green Supermix (Bio-Rad, Hercules, Calif.), 200 nmol/L each primer under the following conditions: 95° C. for 3 minutes followed by 40 cycles of 95° C., 60° C., and 72° C. for 30 seconds each. A standard curve was created for each plate by amplifying 10-fold serially diluted human genomic DNA samples (Novagen. Madison, Wis.). Melting curve analysis was made after each PCR to guarantee that only one product was amplified for all samples.

Amplification was carried out in 96-well plates in an iCycler (Bio-Rad). Each plate consisted of stool DNA samples and multiple positive and negative controls. Each assay was done in duplicate.

The following was performed to compare long DNA (245 bp) and short (45 bp) human DNA in stool for detection of upper and lower GI neoplasms, and to assess the effect of GI tumor site on human DNA levels in stool. Subjects included 33 patients with colorectal cancer, 20 with colorectal adenomas >1 cm, 13 with pancreatic cancer, and 33 colonoscopically-normal controls. Subjects added a preservative buffer to stools at time of collection to prevent post-defecation bacterial metabolism of DNA, and stools were frozen within 8 hours at −80° C. Using a validated quantitative assay for human DNA (Zou et al. Epidemiol Biomarker. Prev., 15:1115 (2006)), 245 bp and 45 bp Alu sequences were amplified from all stools in blinded fashion. Sensitivities for long and short DNA were based on 97 percent specificity cut-offs.

Age medians were 60, 66, 69, and 62 for colorectal cancer, colorectal adenoma, pancreatic cancer, and control groups, respectively; and male/female distributions were 22/11, 9/11, 9/4, 11/21, respectively. In stools from neoplasm and control groups, amplification products were quantitatively greater for short DNA versus long DNA Respective sensitivities by long and short DNA were 66 percent and 62 percent with the 29 distal colorectal neoplasms, 46 percent and 46 percent with the 24 proximal colorectal neoplasms, and 15 percent and 31 percent (p=0.16) with the 13 pancreatic cancers. By Wilcoxan Rank-Sum test, effect of neoplasm site on detection rates was significant for both long DNA (p=0.004) and short DNA (p=0.02). Among colorectal neoplasms, respective sensitivities by long and short DNA were 48 percent and 52 percent with lesions <3 cm, 63 percent and 63 percent with those >3 cm, 64 percent and 61 percent with cancers, and 35 percent and 45 percent with adenomas.

These results demonstrate that short and long DNA can be comparably sensitive for stool detection of GI neoplasms. However, detection rates vary with tumor site, being greatest with the most distal lesions and lowest with the most proximal ones. These results were consistent with substantial luminal degradation of DNA exfoliated from more proximal GI neoplasms.

It was also demonstrated that mutant gene markers in stool can be detected to a greater extent if amplicon size is less than 70 bp, consistent with luminal degradation. Thus, short DNA can serve as a marker per se and as the target size for mutation detection.

Example 7—Use of Fecal Methylated BMP3 as a Neoplasia Marker

Stools from patients with colorectal tumors were found to contain significantly elevated amounts of methylated BMP3 gene copies, but those from normal individuals were found to contain none or only trace amounts. When fecal methylated BMP3 was assayed with an appropriate amplification method, colorectal cancers and premalignant adenomas were specifically detected (FIG. 1). Fecal methylated BMP3 detected a higher percentage of proximal colon tumors than distal tumors, so it can be combined with markers for distal colorectal tumors to create complementary marker panels. Fecal methylated BMP3 was very specific with few false-positive reactions.

Similar results can be obtained using other genes and methods such as those described elsewhere (Zou et al., Cancer Epidemiol Biomarkers Prev., 16(12):2686 (2007)).

Example 8—Detecting Aero-Digestive Cancers by Stool DNA Testing

Tissue samples from patients with confirmed aero-digestive tumors were extracted and sequenced to assess the presence or absence of somatic gene alterations. Germline DNA from the same patients were used as controls. Once an alteration was confirmed, a matched stool sample was tested for that alteration. Two separate methods were utilized to detect the mutation in stool: Allele specific PCR and digital melt curve analysis. For both methods, we focused on amplifying the shortest fragments possible (>100 bp) that have been shown to contain higher levels of the mutant sequence.

Digital Melt Curve (DMC)

We studied 138 patients (69 cases with a GI neoplasm and 69 age/sex-matched asymptomatic controls with normal colonoscopy) by first, identifying a mutation in neoplasm tissue, and then determining if that specific mutation could be detected in stool from that individual. Stools were collected with a stability buffer and frozen at −80° C. until assayed.

Genes commonly mutated in GI neoplasms (TP53, KRAS, APC, CDH1, CTNNB1, BRAF, SMAD4, and P16) were sequenced from DNA extracted from tumor tissue, to identify a target mutation for each case. Target genes were isolated by hybrid capture (Table 7) and the tissue-confirmed somatic mutations were assayed in stool by the digital melt curve method, as described in Example 1. Mutations detected in stool were confirmed by sequencing. Assays were performed blinded.

TABLE 7 Sequence Specific Capture Probes and Primers for AD Cancer Mutation Detection SEQ SEQ ANTISENSE SEQ MUTATION ID SENSE PRIMER  ID PRIMER 2 ID IN TISSUE CAPTURE PROBE No. 1 (5′ TO 3′) No. (5′ TO 3′) No. 12487C>CT:167Q>Q/X ATGGCCATCTACAAGCAGTCAT  49 AGTACTCCCCTGC 128 CTCACAACCTCCG 169 AGCACATGACGGAGGTTGT CCTCAAC TCATGTG 102447_102450het_delTGGT AGAGTGAACCATGCAGTGGAAA  50 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27 AGTGGCATTATAAGCCC CGATTGC GGGCTAT 12410G>GA, 141C>C/Y TTTGCCAACTGGCCAAGACCTA  51 AGTACTCCCCTGC 128 CTCCGTCATGTGC 170 CCCTGTGCAGCTGTG CCTCAAC TGTGACT 102678het_delA CAGATGCTGATACTTTATTACT  52 TCCAGGTTCTTCC 130 CACTCAGGCTGGA  22 TTTGCCACGGAAAGTACT AGATGCT TGAACAA 102594_102598het_delAGAGA AAAGCACCTACTGCTGAAAGAG  53 AGCTCAAACCAAG 131 AGCATCTGGAAGA  28 AGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102644_102646het_insG ATGCTGCAGTTCAGAGGGGTCC  54 GGACCTAAGCAAG 132 CACTCAGGCTGGA  22 AGGTTCTTCCAGATGC CTGCAGTA TGAACAA 102594_102595het_delAG TAAAGCACCTACTGCTGAAAAG  55 AGCTCAAACCAAG 131 AGCATCTGGAAGA  28 AGAGAGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102106het_delT CACAGGAAGCAGATTCTGCAAT  56 CAGACGACACAGG 133 TGCTGGATTTGGT 171 ACCCTGCAAATAGCA AAGCAGA TCTAGGG 102442het_delT TTCAGAGTGAACCATGCAGGGA  57 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27 ATGGTAAGTGGCATTAT CGATTGC GGGCTAT apc 102494C>CT; 1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58 GTGAACCATGCAG  25 AGCTGTTTGAGGA 172 ATGCCACCAAG TGGAATG GGTGGTG apc 102557C>CT; 1450R>R/X CTCAAACAGCTCAAACCAAGTG  59 ACCACCTCCTCAA 134 GCAGCTTGCTTAG 173 AGAAGTACCTAAAAATAAA ACAGCTC GTCCACT apc 102140het_delA AGCAGAAATAAAAGAAAAGTTG  60 CAGACGACACAGG 133 TGCTGGATTTGGT 171 GAACTAGGTCAGCTGA AAGCAGA TCTAGGG apc 102494C>CT; 1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58 GTGAACCATGCAG  25 AGCTGTTTGAGGA 172 ATGCCACCAAG TGGAATG GGTGGTG apc 102554het_delA CTCAAACAGCTCAAACCAGCGA  61 CATGCCACCAAGC  21 GCAGCTTGCTTAG 173 GAAGTACCTAAA AGAAGTA GTCCACT tp53 E5 12647A>AG: 193H>H/R TCTGGCCCCTCCTCAGCGTCTT  62 CAGGCCTCTGATT  36 ACACGCAAATTTC 174 ATCCGAGTGGAAG CCTCACT CTTCCAC tp53 E5 12742G>GA CCTATGAGCCGCCTGAGATCTG  63 CATAGTGTGGTGG 135 AACCACCCTTAAC 175 GTTTGCAACTGGG TGCCCTA CCCTCCT tp53 E5 12706C>CT:213R>R/X ATGACAGAAACACTTTTTGACA  64 GTGGAAGGAAATT 136 CAGTTGCAAACCA 176 TAGTGTGGTGGTG TGCGTGT GACCTCA tp53 E412712A>AG:215S>S/G GAAACACTTTTCGACATGGTGT  65 GTGGAAGGAAATT 136 CAGTTGCAAACCA 176 GGTGGTGCCCTAT TGCGTGT GACCTCA tp53 E4 12388T>TC:134F>F/L CTGCCCTCAACAAGATGCTTTG  66 TGTTCACTTGTGC 137 GCAGGTCTTGGCC 177 CCAACTGGCCAAG CCTGACT AGTTG tp53 E3 11606G>GA:125T>T/T AAGTCTGTGACTTGCACAGTCA  67 GTCTGGGCTTCTT 138 GCCAGGCATTGAA  31 GTTGCCCTGAGGG GCATTCT GTCTCAT tp53 E6 13379C>CT:248R>R/W GCATGGGCGGCATGAACTGGAG  68 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178 GCCCATCCTCACC ACCACCA GGTGAGG tp53 12E3 11326A>AC TCTTTTCACCCATCTACCGTCC  69 ACCTGGTCCTCTG 140 GGGGACAGCATCA 179 (splice site) CCCTTGCCGTCCC ACTGCTC AATCATC tp53 E6 13412G>GT:259D>D/Y CCATCATCACACTGGAATACTC  70 CCTCACCATCATC 141 GGGTCAGAGGCAA  40 CAGGTCAGGAGCC ACACTGG GCAGA tp53 E4 12449G>GT:154G>G/V ACCCCCGCCCGTCACCCGCGTC  71 GTGCAGCTGTGGG 142 CTCCGTCATGTGC 170 C TTGATT TGTGACT tp53 E7 13872G>GT,298E>E/X GGAACAGCTTTGAGGTGTGTGT  72 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 TTGTGCCTGTCCT CGCAAGA CTTGCTT APC 102843C>CG:1545S>S/X TCAGAGCAGCCTAAAGAATGAA  73 ATGCCTCCAGTTC 144 TTTTTCTGCCTCT 180 ATGAAAACCAAGAGAAA AGGAAAA TTCTCTTGG tp53 E4 12392G>GT, 135C>C/F CCTCAACAAGATGTTTTTCCAA  74 TGCCCTGACTTTC 145 CTGCACAGGGCAG 181 CTGGCCAAGACCT AACTCTGT GTCTT APC 102557C>CT: 1450R>R/X CTCAAACAGCTCAAACCAAGTG  59 ACCACCTCCTCAA 134 GCAGCTTGCTTAG 173 AGAAGTACCTAAAAATAAA ACAGCTC GTCCACT tp53 E7 13819G>T:280R>I CCTGTCCTGGGATAGACCGGCG  75 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 CAC ACAGCTT TCCTCTG tp53 13E4 11326A>AC TCTTTTCACCCATCTACCGTCC  69 ACCTGGTCCTCTG 140 GGGGACAGCATCA 179 (splice site) CCCTTGCCGTCCC ACTGCTC AATCATC tp53 E7 13412G>GT:259D>D/Y CCATCATCACACTGGAATACTC  70 CCTCACCATCATC  41 GGGTCAGAGGCAA  40 CAGGTCAGGAGCC ACACTGG GCAGA tp53 E5 12449G>GT:154G>G/V ACCCCCGCCCGTCACCCGCGTC  71 GTGCAGCTGTGGG 142 CTCCGTCATGTGC 170 C TTGATT TGTGACT tp53 E8 13872G>Gt:298E>E/X GGAGCCTCACCACTAGCTGCCC  76 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 CCAGG CGCAAGA CTTGCTT tp53 E8 13813C>CG,278P>P/R GTGTTTGTGCCTGTCGTGGGAG  77 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 AGACCGGCG ACAGCTT TCCTCTG tp53 13851A>AT,291K>K/X GGAAGAGAATCTCCGCTAGAAA  78 GCGCACAGAGGAA 147 TTCTTGTCCTGCT 183 GGGGAGCCTCA GAGAATC TGCTTACC smad4 E2 19049G>GA, 18118A>A/A GTTAAATATTGTCAGTATGCAT  79 AGGTGGCCTGATC 148 TGGATTCACACAG 184 TTGACTTAAAATGTGATAG TTCACAA ACACTATCACA tp53 E8 13777G>GA:266G>G/E TAGTGGTAATCTACTGGAACGG  80 TTTCCTTACTGCC 149 CACAAACACGCAC 185 AACAGCTTTGAGGTG TCTTGCTTC CTCAAAG tp53 E6 12653T>TC:195T>I/T CCTCCTCAGCATCTTACCCGAG  81 CAGGCCTCTGATT  36 ACACGCAAATTTC 174 TGGAAGGAAAT CCTCACT CTTCCAC tp53 E7 13379C>CT:248R>R/W GCATGGGCGGCATGAACTGGAG  68 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178 GCCCATCCTCACC ACCACCA GGTGAGG tp53 E6 12647A>AG:193H>H/R TCTGGCCCCTCCTCAGCGTCTT  62 CAGGCCTCTGATT  36 ACACGCAAATTTC 174 ATCCGAGTGGAAG CCTCACT CTTCCAC tp53 E6 12712A>AG:215S>S/G GAAACACTTTTCGACATGGTGT  65 GTGGAAGGAAATT 136 CAGTTGCAAACCA  76 GGTGGTGCCCTAT TGCGTGT GACCTCA tp53 E8 13872G>GT,298E>E/X GGAGCCTCACCACTAGCTGCCC  76 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 CCAGG CGCAAGA CTTGCTT tp53 E7 13370G>GA:245G>G/S AGTTCCTGCATGGGCAGCATGA  82 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178 ACCGGAGGC ACCACCA GGTGAGG tp53 E4 11580het_delG CTGGGCTTCTTGCATTCTGGAC  83 CCCTTCCCAGAAA  30 ACTGACCGTGCAA 186 AGCCAAGTCTGTGA ACCTACC GTCACAG tp53 E5 12524A>AG,179H>H/R TGCCCCCACCGTGAGCGCTGC  84 TGGCCATCTACAA 150 CTGCTCACCATCG 187 GCAGTCA CTATCTG >tp53 E6 12661G>GT,198E>E/X TCAGCATCTTATCCGAGTGTAA  85 CAGGCCTCTGATT  36 CCAAATACTCCAC 188 GGAAATTTGCGTGTGGA CCTCACT ACGCAAA tp53 E8 13872G>GT:298E>E/X GGAGCCTCACCACTAGCTGCCC  76 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 CCAGG CGCAAGA CTTGCTT apc 102494C>CT;1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58 CAGGAGACCCCAC  19 TGGCAAAATGTAA  20 ATGCCACCAAG TCATGTT TAAAGTATCAGC apc 102557C>CT;1450R>R/X CTCAAACAGCTCAAACCAAGTG  59 CAGGAGACCCCAC  19 TGGCAAAATGTAA  20 AGAAGTACCTAAAAATAAA TCATGTT TAAAGTATCAGC apc 102140het_delA AGCAGAAATAAAAGAAAAGTTG  60 TTCATTATCATCT  15 CGCTCCTGAAGAA  16 GAACTAGGTCAGCTGA TTGTCATCAGC AATTCAA apc 102494C>CT;1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58 CAGGAGACCCCAC  19 TGGCAAAATGTAA  20 ATGCCACCAAG TCATGTT TAAAGTATCAGC apc 102134G>GT;1309E>E/X TGCAAATAGCAGAAATAAAATA  86 TTCATTATCATCT  15 CGCTCCTGAAGAA  16 AAAGATTGGAACTAGGTCA TTGTCATCAGC AATTCAA apc 102554het_delA CTCAAACAGCTCAAACCAGCGA  61 CAGGAGACCCCAC  19 TGGCAAAATGTAA  20 GAAGTACCTAAA TCATGTT TAAAGTATCAGC apc 102852het_insA CTAAAGAATCAAATGAAAAACC  87 GAGCCTCGATGAG  23 TCAATATCATCAT  24 AAGAGAAAGAGGCAGAA CCATTTA CATCTGAATCATC Kras 5571G>GA;12G>G/D GTGGTAGTTGGAGCTGATGGCG  88 AGGCCTGCTGAAA   2 TTGTTGGATCATA   3 TAGGCAAGAGT ATGACTG TTCGTCCAC tp53 E4 12392G>GA;135C>C/Y CCTCAACAAGATGTTTTACCAA  89 TGTTCACTTGTGC 137 GCAGGTCTTGGCC 177 CTGGCCAAGACCT CCTGACT AGTTG tp53 E5 12655C>CT;196R>R/X CCTCCTCAGCATCTTATCTGAG  90 CAGGCCTCTGATT  36 ACACGCAAATTTC 174 TGGAAGGAAATTTGC CCTCACT CTTCCAC tp53 E6 13350G>GA;238C>C/Y TCCACTACAACTACATGTATAA  91 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178 CAGTTCCTGCATGGG ACCACCA GGTGAGG tp53 E6 13420G>GA CACTGGAAGACTCCAGATCAGG  92 CCTCACCATCATC 141 GGGTCAGAGGCAA  40 AGCCACTTGCC ACACTGG GCAGA tp53 E5 12712A.AG;215S>S/G GAAACACTTTTCGACATGGTGT  65 GTGGAAGGAAATT 136 CAGTTGCAAACCA 176 GGTGGTGCCCTAT TGCGTGT GACCTCA Kras 5571G>GA;12G>G/D GTGGTAGTTGGAGCTGATGGCG  88 AGGCCTGCTGAAA   2 TTGTTGGATCATA   3 TAGGCAAGAGT ATGACTG TTCGTCCAC P16(ink4a) E1 19638A>AT GGAGAGGGGGAGTGCAGGCAGC  93 AGCCAGTCAGCCG 151 GAGGGGCTGGCTG 189 GGG AAGG GTC P16(ink4a) E2 23353G>GT;447D>DY CCCAACTGCGCCTACCCCGCCA  94 CACCCTGGCTCTG 152 GGGTCGGGTGAGA 190 CTC ACCAT GTGG P16(ink4a) E1 19638A>AT GGAGAGGGGGAGTGCAGGCAGC  93 AGCCAGTCAGCCG 151 GAGGGGCTGGCTG 189 GGG AAGG GTC p16(ink4a) E2 23402het_delT_ GCCCGGGAGGGCTCCTGGACAC  95 GACCCCGCCACTC 153 CAGCTCCTCAGCC 191 GCTG TCAC AGGTC p16(ink4a) E2 23403C>CA;484F>F/ GCCCGGGAGGGCTTACTGGACA  96 GACCCCGCCACTC 153 CAGCTCCTCAGCC 191 CGCTGGT TCAC AGGTC ctnnb1 25541het_delT CAATGGGTCATATCACAGATTC  97 ATATTTCAATGGG 154 TCAAATCAGCTAT 192 TTTTTTTTAAATTAAAGTAACA TCATATCACAG AAATACGAAACA cdh 1 E9 76435het_delA TCTTATCTCAAAAGAACAACAA  98 GCCATGATCGCTC 155 TCTCAGGGGGCTA 193 AAAAGAGGAATCCTTTAG AAATACA AAGGATT cdh 1 E1 743_744het_ GCGCCCAGCCCTGCGCCCATTC  99 ACTTGCGAGGGAC 156 GAAGAAGGGAAGC 194 insAGCCCTGCGCCCA CTC GCATT GGTGAC cdh 1 E13 8685386854het_insA AAGTAAGTCCAGCTGGCAAAGT 100 CATTCTGGGGATT 157 GGAAATAAACCTC 195 GACTCAGCCTTTGACTT CTTGGAG CTCCATTTTT cdh 1 E14 91472C>CT;751N>N/N AGGATGACACCCGGGACAATGT 101 CTGTTTCTTCGGA 158 CCGCCTCCTTCTT 196 TTATTACTATGATGAAG GGAGAGC CATCATA cdh 1 E15 92868_92896hct_ TTTTTTCTCCAAAGGACTGACG 102 TTCCTACTCTTCA 159 TGCAACGTCGTTA 197 delTTGACTTGAGCCAGCTGCACAGGGGCCTG CTCGGCCTGAAGTG TTGTACTTCAACC CGAGTCA cdh 1 E4 71669*het_delA CAAGCAGAATTGCTCACTTTCC 103 CGTTTCTGGAATC 160 GCAGCTGATGGGA 198 CAACTCCTCTCC CAAGCAG GGAATAA cdh 1 E7 74926G>GA;289A>A/T GGTCACAGCCACAGACACGGAC 104 CCAGGAACCTCTG 161 TGAGGATGGTGTA 199 GATGATGTGAA TGATGGA AGCGATG cdh 1 E1 736_742het_delTGCGCCC AGCCCTGCGCCCCTTCCTCTCC 105 ACTTGCGAGGGAC 156 GAAGAAGGGAAGC 194 CG GCATT GGTGAC p16(ink4a)E1 19638A>AT GGAGAGGGGGAGTGCAGGCAGC  93 AGCCAGTCAGCCG 151 CTCACAACCTCCG 169 GGG AAGG TCATGTG tp53 E4 12365A>AG;126Y>Y/C TTCCTCTTCCTACAGTGCTCCC 106 CACTTGTGCCCTG  33 GCCAGTTGGCAAA 200 CTGCCCTCAAC ACTTTCA ACATCT tp53 E4 12548G>GA TGCTCAGATAGCGATGATGAGC 107 CACATGACGGAGG 162 AACCAGCCCTGTC  34 AGCTGGGGCTG TTGTGAG GTCTCT p16(ink4a)E1 19810T>TG;491I>I/S GGTCGGAGGCCGAGCCAGGTGG 108 TTCCAATTCCCCT 163 CCCAACGCACCGA 201 GTAGA GCAAA ATAGT tp53 E7 13757G>GA GCTTCTCTTTTCCTATCCTAAG 109 GGGACAGGTAGGA 164 AGCTGTTCCGTCC 202 TAGTGGTAATCTACTGG CCTGATTT CAGTAGA tp53 E7 13815G>GC;279G>G/R TTGTGCCTGTCCTCGGAGAGAC 110 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 CGGCG ACAGCTT TCCTCTG tp53 E7 13816G>GA;279G>G/E TGTGCCTGTCCTGAGAGAGACC 111 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 GGCGC ACAGCTT TCCTCTG tp53 E5 12365A>AC,126Y>Y/S TTCCTCTTCCTACAGTCCTCCC 112 CACTTGTGCCCTG  33 GCCAGTTGGCAAA 200 CTGCCCTCAAC ACTTTCA ACATCT tp53 E5 12491A>AT,168H>HL TCTACAAGCAGTCACAGCTCAT 113 TGGCCATCTACAA 150 CTGCTCACCATCG 187 GACGGAGGTTGTGGA GCAGTCA CTATCTG 113 TGGCCATCTACAA 150 TCACCATCGCTAT 203 GCAGTCA CTGAGCA 113 TGGCCATCTACAA 150 AACCAGCCCTGTC  34 GCAGTCA GTCTCT kras 5570G>GC,12G>G/R GTGGTAGTTGGAGCTGATGGCG  88 AGGCCTGCTGAAA   2 TTGTTGGATCATA   3 TAGGCAAGAGT ATGACTG TTCGTCCAC tp53 E7 13370G>GA,245G>G/S AGTTCCTGCATGGGCAGCATGA  82 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178 ACCGGAGGC ACCACCA GGTGAGG apc 102864_102865het_delAG AAATGAAAACCAAGAGAAAGGC 114 TGACAATGGGAAT 165 GGTCCTTTTCAGA 204 AGAAAAAACTATTGATTC GAAACAGA ATCAATAGTTTT tp53 E5 12386T>TC,133M>M/T CCTGCCCTCAACAAGACGTTTT 115 TGTTCACTTGTGC 137 GCAGGTCTTGGCC 177 GCCAACTGGCC CCTGACT AGTTG cdh1 E15 93059G>GA GCTCATCTCTAAGCTCAGGAAG 116 CCAAAGCATGGCT 205 CTCAGGCAAGCTG 206 AGTTGTGTCAAAAATGAGA CATCTCTA AAAACAT tp53 E8 13798G>GA:273R>R/H CGGAACAGCTTTGAGGTGCATG 117 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 TTTGTGCCTGTCCTGGG ACAGCTT TCCTCTG p53 E6, 12698_12701het_delAC TGGAAGGAAATTTGCGTGTGGA 118 GTGGAAGGAAATT 136 AGCTGTTTGAGGA 172 (1 or 2 AC repeats) GTATTTGGATGACAG TGCGTGT GGTGGTG P53 E8, 13824C>CT, 282R>R/W TGTCCTGGGAGAGACTGGCGCA 119 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 CAGAGGAAGAGAAT ACAGCTT TCCTCTG APC 102151G>GA, 1314R>R/R AAGAAAAGATTGGAACTAGATC 120 CAGACGACACAGG 133 GTGACACTGCTGG 207 AGCTGAAGATCCTGTG AAGCAGA AACTTCG P53 E5 12457 G>G/T CGCCCGGCACCCGCTTCCGCGC 121 GTGCAGCTGTGGG 142 CTCCGTCATGTGC 170 CATGGCCA TTGATT TGTGACT p53 E*13812C>CG,278P>P/A GTGTTTGTGCCTGTGCTGGGAG 122 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 AGACCGGCG ACAGCTT TCCTCTG APC 102686het_delA AGGTTCTTCCAGATGCTGATAC 123 CTGCAGTTCAGAG 210 CACTCAGGCTGGA  22 TTTATTACATTTTGC GGTCCAG TGAACAA APC het_delAG between 102594_ GCGAGAAGTACCTAAAAATAAA 124 AGCTCAAACCAAG 131 AGCATCTGGAAGA  28 102603 (1 of 5 AG repeats) GCACCTACTGCTGAA CGAGAAG ACCTGGA APC 102240C>CA,1344S>S/X CAGGGTTCTAGTTTATCTTAAG 125 CCCTAGAACCAAA 166 TGTCTGAGCACCA 208 AATCAGCCAGGCACA TCCAGCA CTTTTGG 102676102680delACATT CCAGATGCTGATACTTTATTTT 126 CTGCAGTTCAGAG 167 CACTCAGGCTGGA  22 GCCACGGAAAGTACTC GGTCCAG TGAACAA 12487C>CT:167Q>Q/X ATGGCCATCTACAAGCAGTCAT  49 AGTACTCCCCTGC 128 CTCACAACCTCCG 169 AGCACATGACGGAGGTTGT CCTCAAC TCATGTG 102447_102450het_delTGGT AGAGTGAACCATGCAGTGGAAA  50 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27 AGTGGCATTATAAGCCC CGATTGC GGGCTAT 12410G>GA,141C>C/Y TTTGCCAACTGGCCAAGACCTA  51 AGTACTCCCCTGC 128 CTCCGTCATGTGC 170 CCCTGTGCAGCTGTG CCTCAAC TGTGACT 10267het_delA CAGATGCTGATACTTTATTACT  52 TCCAGGTTCTTCC 130 CACTCAGGCTGGA  22 TTTGCCACGGAAAGTACT AGATGCT TGAACAA 102594_102598het_delAGAGA AAAGCACCTACTGCTGAAAGAG 127 AGCTCAAACCAAG 131 AGCATCTGGAAGA  28 AGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102776A>AT:1523R>R/X ATGACAATGGGAATGAAACAGA  14 TTTGCCACGGAAA 168 TTTCCTGAACTGG 209 ATCAGAGCAGCCTAAAG GTACTCC AGGCATT 102644_102645het_insG ATGCTGCAGTTCAGAGGGGTCC  54 GGACCTAAGCAAG 132 CACTCAGGCTGGA  22 AGGTTCTTCCAGATGC CTGCAGTA TGAACAA 102594_102595het_delAG TAAAGCACCTACTGCTGAAAAG  55 AGCTCAAACCAAG 131 AGCATCTGGAAGA  28 AGAGAGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102106het_delT CACAGGAAGCAGATTCTGCAAT  56 CAGACGACACAGG 133 TGCTGGATTTGGT 171 ACCCTGCAAATAGCA AAGCAGA TCTAGGG 102442het_delT TTCAGAGTGAACCATGCAGGGA  57 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27 ATGGTAAGTGGCATTAT CGATTGC GGGCTAT apc 102494C>CT;1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58 GTGAACCATGCAG  25 AGCTGTTTGAGGA 172 ATGCCACCAAG TGGAATG GGTGGTG apc 102140het_delA AGCAGAAATAAAAGAAAAGTTG  60 CAGACGACACAGG 133 TGCTGGATTTGGT 171 GAACTAGGTCAGCTGA AAGCAGA TCTAGGG apc 102554het_delA CTCAAACAGCTCAAACCAGCGA  61 CATGCCACCAAGC  21 GCAGCTTGCTTAG 173 GAAGTACCTAAA AGAAGTA GTCCACT

Target mutations were not detected in control stools. Target mutations were detected in stools from 68% (47/69) of patients with a GI neoplasm. Specifically, target mutations were detected in stools from 71% (36/51) of patients with cancer [40% (2/5) with oropharyngeal, 65% (11/17) with esophageal, 100% (4/4) with gastric, 55% (6/11) with pancreatic, 75% (3/4) with biliary or gallbladder, and 100% (10/10) with colorectal] and from 61%(11/18) with precancers [100% (2:2) with pancreatic intraductular papillary mucinous neoplasia and 56% (9/16) with colorectal advanced adenoma]. Mutant copies in genes recovered from stool averaged 0.4% (range 0.05-13.4%) for supracolonic and 1.4% (0.1-15.6%) for colorectal neoplasms, p=0.004 (Table 8).

TABLE 8 Digital Melt Curve Detection of Validated Mutations in AD Cancer Patient Stool Stool Mutation Normal # ID Site Age Gender Tissue Mutation Detection Frequency % Control 1 1163 Head/Neck(pharynx) 73 M tp53 YES 0.8 Neg 2 1250 Head/Neck(pharynx) 49 M tp53 NO Neg 3 1295 Head/Neck(pharynx) 47 F tp53 NO Neg 4 1391 Head/Neck 65 M tp53 TP53 NO (both) Neg 5 1427 Head/Neck 60 M tp53 tp53 YES(p53-1), 0.05 Neg No (p53-2) 1 745 Esophagus 84 F tp53 YES 0.4 Neg 2 769 Esophagus 56 F tp53 YES 0.4 Neg 3 782 Esophagus 55 M tp53 NO Neg 4 789 Esophagus 61 M tp53 YES 1.6 Neg 5 819 Esophagus 53 M tp53 YES 0.2 Neg 6 873 Esophagus 61 M tp53 YES 0.2 Neg 7 906 Esophagus 55 M APC YES 0.8 Neg 8 1049 Esophagus 57 M tp53 NO Neg 9 1064 Esophagus 72 F tp53 NO Neg 10 1067 Esophagus 72 M tp53 YES 0.7 Neg 11 1103 Esophagus 78 M tp53 NO Neg 12 1199 Esophagus 66 M tp53 YES 0.5 NEG 13 1307 Esophagus 51 M tp53 NO NEG 14 1373 Esophagus 76 M tp53 YES 0.5 NEG 15 1414 Esophagus 66 M tp53 YES 0.1 NEG 16 1448 Esophagus 82 M tp53 NO NEG 17 1072 Esophagus tp53 YES 0.4 NEG 1 798 Stomach 81 M cdh1 YES 13.2 NEG 3 1221 Stomach 55 M cdh1 cdh1 YES(both)   8, 1.3 NEG 4 1224 Stomach 75 F smad4 cdh1 YES(smad4), 0.2 NEG No(CDH1) 5 1402 Stomach 56 M APC tp53 YES (p53) 0.1 NEG 1 848 Gall Bladder 67 M tp53 YES 0.1 NEG 2 1315 Gall Bladder 57 F tp53 YES 1.4 NEG 1 1043 Bile Duct 51 F APC NO NEG 2 1554 Bile Duct 77 M cdh1 YES 13.4 NEG 1 757 Pancreatic Cancer 78 M K-ras YES 0.2 NEG in situ 2 1349 Pancreatic Cancer 64 M K-ras YES 0.2 NEG in situ 1 839 Pancreas 69 F tp53 YES 0.2 NEG 2 1204 Pancreas 65 F p16 NO NEG 3 1253 Pancreas 63 F K-ras tp53 Yes(k-ras), 2 NEG No(p53) 4 1400 Pancreas 71 F tp53 K-ras No(both) NEG 5 1547 Pancreas 77 F tp53 NO NEG 6 1217 Pancreas K-ras NO NEG 7 1073 Pancreas K-ras NO NEG 8 532 Pancreas K-ras YES 1 NEG 9 1592 Pancreas K-ras P53 YES (both) 0.3 NEG 10 1695 Pancreas K-ras YES 0.2 NEG 11 1058 Pancreas K-ras P53 APC YES(K-ras) 0.2 NEG 1 438 Colorectal 78 F APC YES 1.2 NEG Cancer 2 446 Colorectal 74 M BRAF YES 0.4 NEG Cancer 3 529 Colorectal 46 M K-RAS YES 1 NEG Cancer 4 489 Colorectal 73 M K-RAS YES 2.6 NEG Cancer 5 549 Colorectal 79 M BRAF YES 1.6 NEG Cancer 6 551 Colorectal 69 M K-RAS YES 5.8 NEG Cancer 7 584 Colorectal 68 M K-RAS YES 1.4 NEG Cancer 8 894 Colorectal 57 M P53 APC YES(p53, 1.6, 5   NEG Cancer APC) 9 998 Colorectal 45 F APC KRAS YES(K-ras, 0.6, 0.8 NEG Cancer APC) 10 1009 Colorectal 65 F P53 YES 12.9 NEG Adenoma 1 513 Colorectal 65 F APC YES 0.1 NEG Adenoma 2 546 Colorectal 61 M APC NO NEG Adenoma 3 547 Colorectal 52 F APC NO NEG Adenoma 4 568 Colorectal 52 M APC YES 7.8 NEG Adenoma 5 578 Colorectal 71 F APC NO NEG Adenoma 6 590 Colorectal 54 F APC YES 3.2 NEG Adenoma 7 701 Colorectal 72 F APC NO NEG Adenoma 8 855 Colorectal 75 M K-RAS YES 0.4 NEG Adenoma 9 860 Colorectal 53 M APC YES 15.6 NEG Adenoma 10 900 Colorectal 64 F APC K-RAS No(both) NEG Adenoma 11 962 Colorectal 56 M K-RAS Yes 1 NEG Adenoma 12 965 Colorectal 82 M APC K-RAS No (both) NEG Adenoma 13 991 Colorectal 79 M APC K-RAS YES(K-ras), 0.2 NEG Adenoma No(APC) 14 1135 Colorectal 59 M K-RAS YES 13 NEG Adenoma 15 1231 Colorectal 50 M APC NO NEG Adenoma 16 1559 Colorectal K-RAS YES 1 NEG Adenoma

We also performed an initial pilot study with 10 stool samples from patients with confirmed bile duct cancers to determine if DMC technology could detect mutations in k-ras, a well characterized gene known be mutated in this population. K-ras mutations were detected in stools for 3/10 or 4/10 bile duct cancers (depending on mutation score of 5 or 3, respectively) (Table 9). As K-ras is mutant in 30-40% of bile duct cancers, these results indicate that the detection assay is picking up the appropriate proportion of cancer samples.

TABLE 9 K-ras mutation scores for patients with bile duct cancer. K-ras Mutation Mutation Detected Sample # Pathology Score A B 520 BD Cancer 0 528 BD Cancer 0 559 BD Cancer 0 558 BD Cancer 1 Codon 12 GAT 515 BD Cancer 2 Codon 12 GAT 543 BD Cancer 2 Codon 13 GAC 806 BD Cancer 3 Codon 12 TGT 539 BD Cancer 5 Codon 12 GAT Codon 13 GGA 512 BD Cancer 6 Codon 12 GAT Codon 12 GAT 725 BD Cancer 25 Codon 13 GAC Codon 12 GAT; Codon 12 GTT Allele Specific PCR

The allele specific-PCR assay was a modified version of a previously published method (e.g., Cha et al., Mismatch Amplification Mutation Assay (MAMA): Application to the c-H-ras Gene PCR Methods and Applications, 2: 14-20 (1992) Cold Spring Harbor Laboratory). TP53 gene fragments were captured from stool DNA samples with probes specific to mutations identified in the matched tissue (Table 7). Copy numbers were assessed by qPCR. Samples were adjusted to 10,000 fragments each and amplified with allele specific primer sets.

Sample F Primer R Primer A745 GACAGAAACACTTTAT CGGCTCATAGGG (SEQ ID No: 211) (SEQ ID NO: 217) A848 ACACTTTTCGACAAG AAACCAGACCTCAG (SEQ ID No: 212) (SEQ ID No: 218) A789 CCTCAACAAGATAC CAGCTGCACAGG (SEQ ID No: 213) (SEQ ID NO: 219) A782 GCCGCCTGAAA AGACCCCAGTTGC (SEQ ID No: 214) (SEQ ID No: 220) A873 GCGGCATGAAAT TTCCAGTGTGATGAT (SEQ ID No: 215) (SEQ ID NO: 221) A769 CCCCTCCTCAGAG CTTCCACTCGGATAA (SEQ ID No: 216) (SEQ ID No: 222) The Forward Primer in Each Case is Specific for Each TP53 Mutation. Esophagus and Stomach

Targeting mutations found in esophageal cancers or those from gastroesophageal junction (on p53, APC, or K-ras), the same mutation was detected by allele-specific PCR in matched stools from five of five (100%) cancers but in none of the controls (Table 10). The threshold cycle (Ct), designates the PCR cycle at which the product enters the exponential phase of amplification.

Gallbladder

Targeting a mutation confirmed in a gallbladder cancer, the same mutation was found in the matched stool from that patient using allele-specific PCR (Table 10).

TABLE 10 Quantitative Mutant Allele Specific-PCR Results for Matched Aero- digestive Cancers Sample Gene # fragments Ct A769 esophageal/gastric cancer p53 10K 71 N normal p53 10K >80 A782 esophageal/gastric cancer p53 10K 38.8 N normal p53 10K 44.1 A745 esophageal/gastric cancer p53 30K 42.5 N normal p53 30K 45.6 A873 esophageal/gastric cancer p53 30K 37.8 N normal p53 30K 40.4 A848 gall bladder cancer p53 10K 22.4 N normal p53 10K 36.3 A789 esophageal/gastric cancer p53 10K 25.9 N normal p53 10K 28.7

Example 9—Candidate Stool Polypeptide Markers Identified for Colorectal Cancer and Precancerous Adenomas

The following list of polypeptides were identified by a statistical analysis model using all data generated from mass spectral of fecal protein extracts: β2-macroglobulin, compliment C3 protein, serotransferrin, haptoglobin, carbonic anhydrase 1, xaa-pro dipeptidase, leukocyte elastase inhibitor, hemoglobin, glucose-6-phosphate, and catalase. This list of polypeptides is in order of difference from normal. Thus, the mean spectral abundance for β2-macroglobulin is most different from normal for cancer and adenoma.

The statistical significance of relative poly peptide abundance between normal, adenoma, and colorectal cancer (CRC) was obtained using normalized spectral count data from a zero inflated poisson regression model as an offset term in the protein specific differential expression analysis. The differential expression analysis also incorporated the zero inflated poisson regression model. Polypeptides were then ranked according to their statistical significance and whether the expression profile followed the clinically relevant pattern of Normal <Adenoma <CRC. Using a rule that a positive test required that any three of top six markers be positive, the sensitivity and specificity of this panel were both 100% in a training set.

The listed polypeptides can be used individually or in any combination to detect colorectal cancer or precancerous adenomas.

Example 10—Identification of Polypeptide Markers for Pancreatic Cancer

Potential polypeptide markers for pancreatic cancer prediction were identified. Utilizing a Scaffold (Proteome Software) side-by-side comparison of spectral abundances, ratios of the spectral counts of carboxypeptidase B (CBPB1_HUMAN) and Carboxypeptidase A1 (CBPA1_HUMAN) were compared. A value for carboxypeptidase B/Al of 0.7 or higher was predictive of pancreatic cancer (m normal stools, an average ratio of 2:3 B/Al was observed). Specificity for training data was 100% with a sensitivity of 88%, while sensitivity from a validation set was 82% at the same specificity.

Example 11—Use of Fecal Methylated ALX4 as a Neoplasia Marker

Stools from patients with colorectal tumors were found to contain significantly elevated amounts of methylated ALX4 gene copies, but those from normal individuals were found to contain none or only trace amounts. When fecal methylated ALX4 was assayed with an appropriate amplification method, colorectal cancers and premalignant adenomas were specifically detected. At 90% specificity, fecal methylated ALX4 detected 59% colorectal cancer and 54% premalignant adenomas, allowing for the detection of both colorectal cancer and premalignant adenomas.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

What is claimed is:
 1. A method for characterizing a biological sample comprising: (a) obtaining a biological sample from a human individual; (b) measuring a K-ras mutation score within the obtained biological sample through exposing a portion of the biological sample to one or more K-ras specific primers and determining the K-ras mutation score by performing a digital melt curve analysis or quantitative allele-specific PCR, wherein the K-ras specific primers comprise a nucleic acid sequence selected from the group consisting of SEQ ID NOs: 2, 3, 4, 5, 6, 7 and 8; (c) measuring a methylation level of a CpG site for BMP3 in a portion of the obtained biological sample through treating genomic DNA in the biological sample with bisulfite; amplifying the bisulfite-treated genomic DNA using primers specific for BMP3, and determining the methylation level of the CpG site by methylation-specific PCR, quantitative methylation-specific PCR, methylation-sensitive DNA restriction enzyme analysis, quantitative bisulfite pyrosequencing, or bisulfite genomic sequencing PCR; (d) comparing the K-ras mutation score and the BMP3 methylation level to a corresponding set of control samples without colorectal cancer; and (e) determining that the individual has colorectal cancer when 1) the measured K-ras mutation score is higher than in the control sample, and 2) the measured BMP3 methylation level is higher than in the control sample.
 2. The method of claim 1, wherein the biological sample is a stool sample, a tissue sample, a blood sample, or a urine sample. 